Performance mode adjustment based on activity detection

ABSTRACT

Disclosed herein are techniques related to device performance mode adjustment based on activity detection. In some embodiments, the techniques involve detecting, based on processing sensor data obtained from one or more gesture sensors, an activity in which a user of the one or more gesture sensors is engaged. The techniques further involve generating information corresponding to the detected activity in which the user of the one or more gesture sensors is engaged. The techniques also involve controlling, based on providing the generated information to a device for monitoring a physiological characteristic of the user, adjustment of a performance mode of the device for monitoring the physiological characteristic of the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/886,360, filed May 28, 2020, which is a continuation-in-part of U.S.patent application Ser. No. 16/667,641, filed Oct. 29, 2019, now U.S.Pat. No. 11,367,516, which claims the benefit of U.S. provisional patentapplication No. 62/753,819, filed Oct. 31, 2018. U.S. patent applicationSer. No. 16/886,360 is also a continuation-in-part of U.S. patentapplication Ser. No. 16/667,650, filed Oct. 29, 2019, which claims thebenefit of U.S. provisional patent application No. 62/753,819, filedOct. 31, 2018. U.S. patent application Ser. No. 16/886,360 also claimsthe benefit of U.S. provisional patent application No. 62/932,370, filedNov. 7, 2019.

TECHNICAL FIELD

This disclosure relates generally to sensor technology and moreparticularly to device performance mode adjustment based on activitydetection.

BACKGROUND

For some medical conditions, such as Type 1 diabetes, the scheduling anddosing of medication such as insulin depends on various factors such asthe patient's current blood sugar levels and whether the patient iseating or drinking, and the contents of what is being consumed. Thus,knowing when someone has started, or is about to start, eating ordrinking is relevant in the treatment of individuals with diabetes whoare on an insulin regimen. Other parameters that further quantify theeating or drinking activities, such as the duration or pace of eating ordrinking can also be relevant as the medication regimen might vary basedon duration and/or pace.

Millions of people have Type 1 diabetes, a condition wherein a person'sbody does not produce insulin. The human body breaks down consumedcarbohydrates into blood glucose, which the body uses for energy. Thebody needs to convert that blood glucose from the bloodstream intoglucose the cells of the body and does this using the hormone insulin. Aperson with Type 1 diabetes does not generate their own insulin insufficient quantities to regulate blood sugar and may require insulininjections to maintain safe blood sugar levels.

Insulin can be provided by a micrometering system that injects aspecific amount of insulin over time. For example, a Type 1 diabetespatient might need to regularly check their blood sugar levels andmanually inject the correct dosage of insulin needed at the start of ameal so that their body is able to convert the glucose that is put intotheir bloodstream as a result of a meal into glucose stored into bodycells. Both overdosing and underdosing can lead to adverse conditionsand long-term complications. Manual management of a microdosing systemor injecting insulin are treatment regimens and often require timing anddosing that can vary. This can make managing the disease difficult,especially where the patient is a younger child.

One approach is a “hybrid” closed-loop dosing system that makescontinuous, or periodic near-continuous, blood glucose readings andmicrodoses the patient autonomously based on those readings, except whenthe patient eats or drinks. To deal with the latter, the hybrid systemaccepts patient input signaling when the patient is going to starteating or drinking. Without this added information, the dosing systemwould be too slow to respond, as there are considerable delays inglucose readings and insulin diffusion. Manual meal announcementsintroduce a significant burden on the patient and poor complianceresults in degraded glycemic control. Missed pre-meal insulin bolusesare a significant contributor to poor glycemic control in Type 1diabetes patients.

Improved methods and apparatus for automated eating or drinking eventdetection and signaling a dosing system and/or reminder messaging topatients, caregivers, and health professionals are needed.

BRIEF SUMMARY

Disclosed herein are techniques related to device performance modeadjustment based on activity detection. The techniques may be practicedusing a processor-implemented method; a system comprising one or moreprocessors and one or more processor-readable media; and one or morenon-transitory processor-readable media.

In some embodiments, the techniques involve detecting, based onprocessing sensor data obtained from one or more gesture sensors, anactivity in which a user of the one or more gesture sensors is engaged.The techniques may further involve generating information correspondingto the detected activity in which the user of the one or more gesturesensors is engaged. The techniques may also involve controlling, basedon providing the generated information to a device for monitoring aphysiological characteristic of the user, adjustment of a performancemode of the device for monitoring the physiological characteristic ofthe user.

The following detailed description together with the accompanyingdrawings will provide a better understanding of the nature andadvantages of the present invention.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an event monitoring system.

FIG. 2 illustrates a process to provide for user intervention.

FIG. 3 is an illustrative example of an environment in accordance withat least one embodiment.

FIG. 4 is an illustrative example of an environment that includescommunication with at least one additional device over the internet inaccordance with at least one embodiment.

FIG. 5 is an illustrative example of an environment where a food intakemonitoring and tracking device communicates directly with a base stationor an access point in accordance with at least one embodiment.

FIG. 6 is an illustrative example of a high-level block diagram of amonitoring and tracking device in accordance with at least oneembodiment.

FIG. 7 is an illustrative example of a block diagram of a monitoring andtracking device in accordance with at least one embodiment.

FIG. 8 shows an example of a machine classification system in accordancewith at least one embodiment of the present disclosure.

FIG. 9 shows an example of a machine classification training subsystemin accordance with at least one embodiment of the present disclosure.

FIG. 10 shows an example of a machine classification detector subsystemin accordance with at least one embodiment of the present disclosure.

FIG. 11 shows an example of a machine classification training subsystemthat uses, among other data, non-temporal data.

FIG. 12 shows an example of a machine classification detector subsystemthat uses, among other data, non-temporal data.

FIG. 13 shows an example of a training subsystem for an unsupervisedclassification system in accordance with at least one embodiment of thepresent disclosure.

FIG. 14 shows an example of a detector subsystem for an unsupervisedclassification system in accordance with at least one embodiment of thepresent disclosure.

FIG. 15 shows an example of a classifier ensemble system.

FIG. 16 shows an example of a machine classification system thatincludes a cross-correlated analytics sub-system.

FIG. 17 shows a high level functional diagram of a monitoring system ofa variation similar to that of FIG. 1 , in accordance with anembodiment.

FIG. 18 shows a high level functional diagram of a monitoring system inaccordance with an embodiment that requires user intervention.

FIG. 19 shows a high level functional diagram of a medication dispensingsystem.

FIG. 20 is an illustrative example of a machine learning system thatmight be used with other elements described in this disclosure.

FIG. 21 is a schematic functional diagram of an exemplary embodiment ofa medication dispensing system.

FIG. 22 is a state diagram that applies to an exemplary embodiment of amedication dosing calculation unit utilized by a medication dispensingsystem.

FIG. 23 is a simplified block diagram representation of an embodiment ofa gesture-informed physical behavior event detector system.

FIG. 24 is a diagram that schematically depicts output of a gesturerecognizer unit over time.

FIG. 25 is a diagram that schematically depicts output of a gesturerecognizer unit over time.

FIG. 26 is a state diagram that applies to an exemplary embodiment of aphysical behavior event detector.

FIG. 27 is a simplified block diagram representation of functionalcomponents of a medication dispensing system.

FIG. 28 is a flow chart that illustrates an exemplary embodiment of agesture-based physical behavior detection process.

FIG. 29 is a flow chart that illustrates an exemplary embodiment of aprocess for controlling operation of a medication dispensing system,based on a predicted occurrence of a behavior event.

FIG. 30 is a flow chart that illustrates an exemplary embodiment of aprocess for controlling operation of a medication dispensing system,which includes the adjustment of a confidence level for a detectedgesture-based behavior event.

FIG. 31 is a flow chart that illustrates an exemplary embodiment of aprocess for controlling operation of a medication dispensing system,which includes the adjustment of criteria used to determine whether agesture-based behavior event has occurred.

FIG. 32 is a flow chart that illustrates an exemplary embodiment of aprocess for controlling operation of a medication dispensing system,which includes the adjustment of at least one sensor system operatingparameter in response to the detection of a gesture-based behaviorevent.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Various examples are provided herein of devices that a person would useto monitor, track, analyze and provide feedback on food intake, theintake process and timing and other relevant aspects of a person'seating, drinking and other consumption for various ends, such asproviding diet information and feedback. The data related to food intakeprocess might include, timing of the eating process, pace of eating,time since last food intake event, what is eaten, estimates of thecontents of what is eaten, etc. Such devices can be integrated with amedication dosing system.

Overview

As will be described in greater detail herein, a patient managementsystem comprising a novel digital health app and a wearable sensingapparatus that interacts with the app can provide for a fully autonomousartificial pancreas system and that can dramatically improve quality oflife for people with Type 1 diabetes. This patient management system canremind patients to take or administer their medication but can alsoprovide for hybrid or autonomous management of medication dosing, suchas for example insulin dosing. This patient management system canpromote mindful eating and proper hydration.

Through a combination of fine motor detection and artificialintelligence technology, the patient management system detects highimpact moments and allows individuals to better manage their health, allbased on insights that are captured automatically and non-intrusivelyfrom analyzing their wrist movements and other sensor inputs. Responseactions might include sending bolus reminders to patients and/or theircaregivers. Early meal detection capabilities can provide uniqueinsights into eating behaviors are critical components towards therealization of an autonomous artificial pancreas system.

The patient management system might have an open data and softwarearchitecture. This might allow for other companies, researchers anddevelopers to make use of the patient management system and the data itgenerates, perhaps via an API that allows for seamless integration withthird-party applications and platforms.

In a specific use, the patient management system serves as a mealtimemedication reminder that messages the patient as to the need to initiatean insulin dose. The patient management system might have a multi-levelmessaging capability. For example, where the patient is messaged aboutthe need to take an action, and the patient does not respond, thepatient management system might send a message to a caregiver toindicate that the patient did or did not take the expected action inresponse to the detection of the start of an eating event or did nottake action for a mealtime insulin administration. A fully autonomousclosed-loop artificial pancreas system, can be in part enabled by thepatient management system and its ability to provide automated real-timeor near real-time information of a patient's eating behavior, coupledwith novel control processes, thus potentially enabling a true “set andforget” closed-loop insulin delivery system.

The patient management system might be useful for a person concernedabout their diet for other reasons. People with Type 1 diabetes areusually on an insulin therapy where, based on their food intake andother factors, they administer the proper insulin dosage. While thecause of Type 1 diabetes may not be directly linked to a person's eatingbehavior, a person with Type 1 diabetes needs to carefully track his orher food intake in order to manage his or her insulin therapy. Suchpatients will also benefit from easier to use and more discreet methodsfor food intake tracking. In some embodiments of the patient managementsystem, the sensing device is part of a feedback-driven automatedinsulin delivery therapy system. Such a system might include continuousmonitoring of a patient's glucose levels, a precision insulin deliverysystem, and the use of insulin that has a faster absorption rate, thatwould further benefit from information that can be extracted fromautomated and seamless food intake tracking, such as the tracking ofcarbohydrates and sugar intake. The devices might also be useful forwellness programs and the like.

Data Collection

Data can be obtained from some stationary device having sensors andelectronics, some mobile device having sensors and electronics that iseasily moved and carried around by a person, and/or from wearabledevices having sensors and electronics that a person attaches to theirperson or clothing, or is part of the person's clothing. In general,herein such devices are referred to as sensing devices. The data mightbe raw sensor data provided by the sensors capable of outputting data,or the data might be processed, sampled, or organized in some way so asto be data derived from the outputs of sensors.

Herein, the person having such a device and who's consumption is beingmonitored is referred to as the user but it should be understood thatthe device might be used unchanged in situations where the personconsuming, the person monitoring, and the person evaluating feedbackneed not all be the same person. Herein, what is consumed is referred toas food intake, but it should be clear that these devices can be used tomore generally track consumption and consumption patterns. A behaviortracking/feedback system as described herein might comprise one or morewearable devices and might also comprise one or more additional devicesthat are not worn. These additional devices might be carried by thewearer or kept nearby so that they can communicate with the wearabledevices. The behavior tracking/feedback system might also compriseremote elements, such as a remote cloud computing element and/or remotestorage for user information.

A wearable device might be worn at different locations on the wearer'sbody (i.e., the person monitoring their behavior) and the wearabledevice might be programmed or configured to account for thosedifferences, as well as differences from wearer to wearer. For example,a right-handed person may wear the device around his right wrist whereasa left-handed person may wear the device around his left wrist. Usersmay also have different preferences for orientation. For example, someusers may want the control buttons on one side, whereas other users mayprefer the control buttons on the opposite side. In one embodiment, theuser may manually enter the wrist preference and/or device orientation.

In another embodiment, the wrist preference and/or device orientationmay be determined by asking the user to perform one or more pre-definedgestures and monitoring the sensor data from the wearable devicecorresponding to the user performing the pre-defined gesture or set ofgestures. For example, the user may be asked to move his hand towardshis mouth. The change in accelerometer sensor readings across one ormore axes may then be used to determine the wrist and deviceorientation. In yet another example, the behavior tracking/feedbacksystem may process the sensor readings from the wearable device whilethe user is wearing the device for a certain duration of time.Optionally, the behavior tracking/feedback system may further combinethe sensor readings with other data or metadata about the wearer, toinfer the wrist and device orientation. For example, the behaviortracking/feedback system may monitor the user for one day and record theaccelerometer sensor readings across one or more of the axes.

Since the movement of the lower arm is constrained by the elbow andupper arm, some accelerometer readings will be more frequent than othersbased on the wrist and device orientation. The information of theaccelerometers can then be used to determine the wrist and/or deviceorientation. For example, the mean, minimum, maximum and/or standarddeviation of the accelerometer readings could be used to determine thewrist and/or device orientation.

In some embodiments, sensing devices can sense, without requiring userinteraction, the start/end of a food intake event, the pace of eating,the pace of drinking, the number of bites, the number of sips, theestimation of fluid intake, and/or estimation of portion sizing.Operating with less human intervention, no human intervention, or onlyintervention not apparent to others will allow the devices to scale wellwith different meal scenarios and different social situations. Sensingmight include capturing details of the food before it is consumed, aswell as user actions that are known to accompany eating, such asrepeated rotation of an upper arm or other hand-to-mouth motions.Sensors might include an accelerometer, a gyroscope, a camera, and othersensors.

Using the devices can provide a person with low friction-of-use todetect, quantify, track and provide feedback related to the person'sfood intake content as well as the person's food intake behavior. Suchmethods have the potential of preventing, treating and, in certaincases, even curing diet-related diseases. Such devices can improveefficacy, accuracy and compliance, and reduce the burden of usage and toimprove social acceptance. The devices can operate autonomously with no,or very minimal, human intervention, and do not interfere in an invasiveor otherwise significant negative way with a person's normal activitiesor social interactions or intrude on the person's privacy. The devicesare able to handle a wide range of meal scenarios and dining settings ina discreet and socially-acceptable manner, and are capable of estimatingand tracking food intake content and quantity as well as other aspectsof eating behavior. The devices can provide both real-time andnon-real-time feedback to the person about their eating behavior, habitsand patterns.

It is generally known and understood that certain eating behaviors canbe linked to, triggered by or otherwise be influenced by physical,mental or environmental conditions such as for example hunger, stress,sleep, addiction, illness, physical location, social pressure, andexercise. These characteristics can form inputs to the processingperformed by or for the devices.

A food intake event generally relates to a situation, circumstance oraction whereby a person eats, drinks or otherwise takes into his or herbody an edible substance. Edible substances may include, but are notlimited to, solid foods, liquids, soups, drinks, snacks, medications,vitamins, drugs, herbal supplements, finger foods, prepared foods, rawfoods, meals, appetizers, main entrees, desserts, candy, breakfast,sports or energy drinks. Edible substances include, but are not limitedto, substances that may contain toxins, allergens, viruses, bacteria orother components that may be harmful to the person, or harmful to apopulation or a subset of a population. Herein, for readability, food isused as an example of an edible substance, but it should be understoodthat other edible substance might be used instead of food unlessotherwise indicated.

Eating habits and patterns generally relate to how people consume food.Eating habits and patterns may include, but are not limited to, the paceof eating or drinking, the size of bites, the amount of chewing prior toswallowing, the speed of chewing, the frequency of food intake events,the amount of food consumed during a food intake event, the position ofthe body during a food intake event, possible movements of the body orof specific body parts during the food intake event, the state of themind or body during a food intake event, and the utensils or otherdevices used to present, handle or consume the food. The pace of eatingor drinking might be reflected in the time between subsequent bites orsips.

Triggers generally relate to the reasons behind the occurrence of a foodintake event, behind the amount consumed and behind how it is consumed.Triggers for food intake events and for eating habits or patterns mayinclude, but are not limited to, hunger, stress, social pressure,fatigue, addiction, discomfort, medical need, physical location, socialcontext or circumstances, odors, memories or physical activity. Atrigger may coincide with the food intake event for which it is atrigger. Alternatively, a trigger may occur outside the food intakeevent window, and might occur prior to or after the food intake event ata time that may or may not be directly related to the time of the foodintake event.

In some embodiments of the sensing device or system, fewer than all ofthe features and functionality presented in this disclosure areimplemented. For example, some embodiments may focus solely on detectionand/or processing and tracking of the intake of food without intendingto steer the user to modify his or her food intake or without tracking,processing or steering eating habits or patterns.

In many examples herein, the setting is that an electronic device isprovided to a user, who wears the electronic device, alone or while itis in communication with a nearby support device that might or might notbe worn, such as a smartphone for performing operations that the wornelectronic device offloads. In such examples, there is a person wearingthe electronic device and that person is referred to as the “wearer” inthe examples and the system comprises a worn device and may includeother components that are not worn and are nearby and components thatare remote, preferably able to communicate with the worn device. Thus,the wearer wears the electronic device, and the electronic deviceincludes sensors, which sense environment about the wearer. That sensingcan be of ambient characteristics, body characteristics, movement andother sensed signals as described elsewhere herein.

In many examples, functionality of the electronic device might beimplemented by hardware circuitry, or by program code instructions thatare configurable to be executed by at least one hardware processordevice in the electronic device, or a combination. Where it is indicatedthat a processor does something, it may be that the processor is causedto perform tasks as a consequence of executing instructions read from aninstruction memory (e.g., a storage medium that includes program codeinstructions) wherein the instructions provide for performing thatthing. In this regard, the program code instructions are configurable tocause the processor (or the host device) to perform certain methods,processes, or functions as defined by the executed instructions. Whileother people might be involved, a common example here is where thewearer of the electronic device is using that electronic device tomonitor their own actions, such as gestures, behavior events comprisinga sequence of gestures, activities, starts of activities or behaviorevents, stops of activities or behavior events, etc. Where it isdescribed that a processor performs a particular process, it may be thatpart of that process is done separate from the worn electronic device,in a distributed processing fashion. Thus, a description of a processperformed by a processor of the electronic device need not be limited toa processor within the worn electronic device, but perhaps a processorin a support device that is in communication with the worn electronicdevice.

Patient Management System

A patient management system might comprise a novel digital health appthat runs on a device such as a smartphone or a dedicated device thatcan communicate with a wearable sensing apparatus worn by a person. Thewearable sensing apparatus interacts with the app to provide for hybridor autonomous management of insulin dosing and reminders.

In an example of a use of such a system, a person—referred to herein asa “patient”—has been diagnosed with Type 1 diabetes that requiresexternal insulin to be provided to the patient. The insulin could begiven in the form of injectable insulin using for example a pen orsyringe, or via an insulin pump or a similar insulin dosing anddispensing device that is attached to the patient's body to introducemeasured amounts of insulin into the patient's body. The dose amount andtiming can be a function of when the patient is eating, begins eating,is imminently going to be eating, has been eating and will continue toeat, or has finished eating. A wearable device, capable of discerninggestures from movements and sensor data outputs, can determine, possiblywithout specific patient intervention, when an eating event has started,or is about to start, as well as the pace, duration and likelyconclusion of an eating event. From this determination, the wearabledevice (or an ancillary device in communication with the wearabledevice, such as a full-function smartphone) would send a signal to theinsulin dosing and dispensing device indicating some details of theeating/drinking and/or would send a message to the patient, a caretaker,and/or a health professional. In a different embodiment, the wearabledevice may communicate with an ancillary device such as a full-functionsmartphone, the smartphone may process the information received from thewearable, possibly combined with inputs from one or more other devicessuch as a continuous glucose monitoring (“CGM”) device to determineinsulin dosing amounts and/or timing and communicate dosing informationback to the patient or to an insulin dispensing device that is attachedto the patient.

For example, the wearable device might determine that the patient hasstarted eating and from the pace of eating and a determined likelyduration of the event, could signal to a dosing and delivery device someinformation about the eating event, which the delivery device could useto start a delivery of insulin to the patient. In addition, or instead,the wearable device could send a message relating to the eating eventand the parameters measured. For example, the wearable device mightcommunicate with a nearby smartphone that is running an app that ispaired to that wearable device and send a message, perhaps as a cellulartelephone network text message (e.g., SMS message) to a prestored numberassigned to the patient, where the message says something like “A foodevent has been detected. Be sure to activate your insulin dosing anddelivery device to dose insulin.”

If the patient is the operator of the smartphone, perhaps the app couldmessage the patient directly, without having to send a network textmessage. The message could be displayed on the phone, on the wearabledevice of the patient or on another electronic device with which thepatient interacts. In some cases, it might be useful to have more thanone recipient of the message. For example, where the patient needs oruses caregiver assistance, whether due to age (young or old) or forother reasons, the wearable device messaging can also go to thecaregiver. Where useful or needed, this information can be provided to ahealth care professional, perhaps to monitor patient regimen compliance.

As used herein, an event might be described as an “eating event” or a“food intake event” rather than an eating event and/or a drinking event”for readability, but it should be understood that, unless otherwiseindicated, a teaching herein related to an eating event or food intakeevent may equally apply to a drinking event.

Gesture-sensing technology can be used to automatically detect a gestureevent without user prompting or interaction, such as gestures toindicate events when someone is eating or drinking from their handgestures using motion sensors (accelerometer/gyroscope) in a wrist-wornwearable device or ring. This detection can occur in real time to deducekey insights about the consumption activity such as, for example, astart time of a consumption event, an end time of the consumption event,a consumption method, metrics associated with pace of consumption,metrics associated with quantities consumed, and metrics associated withfrequency of consumption, location of consumption, etc. Thegesture-sensing technology can be used for other activities andbehaviors such as smoking, dental hygiene, hand hygiene, etc.

The gesture-sensing technology can automatically (i.e., withoutrequiring user intervention) detect when someone is eating or drinkingfrom their hand gestures using motion sensors (accelerometer/gyroscope)in a wrist-worn wearable device or ring. The motion sensors may also beembedded in another device generally worn on the arm, around the wrist,hand or fingers. The motion sensors could also be embedded into a tattooor implanted into the patient's body. This detection can occur in realtime or in near real time. The worn device might be combined withoff-device processing and communication functionality, as might be foundin a portable communication device. This off-device processing might beused for more complex processing tasks, such as deducing insights abouta consumption activity such as, for example, start time, end time,consumption method, metrics associated with pace of consumption, metricsassociated with quantities consumed, metrics associated with frequencyof consumption, location of consumption, etc.

The start of each meal can be a critical moment for someone living withType 1 diabetes. In an example of disease management, at the beginningof each meal, the patient (or caregiver, etc.) needs to decide whetherand how much insulin to inject and an action needs to be taken based onthat decision (e.g., an action of injecting insulin).

The patient management system can provide for early meal detectioncapabilities and provide powerful “nudges” that can help the patientand/or insulin dosing device with information for decision making andaction taking. For example, the patient management system might sendreal-time reminders to administer insulin at start of meal. Forgettingto bolus is one of the main reasons for poor glycemic control,particularly among adolescents. The patient management system can promptpatients to check their blood sugar level at the start and/or end of ameal and can support easy logging. Logging might be handled from thewearable component of the system and it might be as discrete as thepatient performing a wrist gesture to signal that the requested action(e.g., administering insulin or checking blood sugar level) has beenperformed or other movements that can serve as data entry actions.

Health care providers can survey their patients “in-the-moment”,yielding more accurate and actionable insights (e.g., assess difficultyestimating carbs, emotions, need for help).

The patient management system can track, more generally, eating anddrinking activities with seconds'-level accuracy, making it possible,for example, to correlate blood glucose levels and bolusing (insulinadministration) actions back to exact eating times. This can be used inpersonalized nutrition research and in the development of personalizednutrition plans. This information may be an integral part ofpersonalized diabetes care pathways.

The patient management system can be used in studies of the impact ofreal-time bolus reminders on medication adherence and glycemic control.Data sharing could occur in real-time or not in real-time. Data sharingcould be with caregivers, health care professionals or, with appropriateprivacy safeguards, provided as part of a larger data set to researchersand medical device developers.

One part of the patient management system is the app, which mightprovide a user interface to be used by the patient. This app couldprovide the ability to send real-time bolus reminders to the patient anda monitoring service that allows real-time alerts (notifications ortext) to be sent to one or more destinations (e.g., telephone numbers,e-mail addresses, URLs, etc.) for remote caregivers and others. Thiscould, for example, allow parents to remotely monitor their young Type 1diabetes children's eating activities and responses to messages oralerts.

In some approaches, a patient manually informs an insulin deliverysystem when he or she is eating or is about to eat. Based on this input,the insulin delivery system will then start insulin delivery in one ormultiple doses. This is not ideal, since the user needs to take theaction of informing the insulin delivery system. It would be better ifno action by the user were required. Alternatively, an insulin deliverysystem could infer the occurrence of a food intake event from monitoringchanges in the patient's blood glucose levels. Direct blood glucoselevel measurement can be impractical. Interstitial glucose levels can bemeasured automatically and periodically using a continuous glucosemonitoring device and those levels are often used as a proxy for bloodglucose levels. However, interstitial glucose level measurements can bedelayed by 20 minutes or more. This is too late to achieve good glycemiccontrol and so interstitial fluid measurements can be less than ideal toinform an insulin delivery system.

There is a need for automatic detection of food intake events to informan insulin delivery system and allow insulin delivery systems to operatewithout or with reduced human intervention. The patient managementsystem can be used as a medication reminder system in general or morespecifically, an insulin therapy system.

An example of a patient management system will now be described.

Upon the detection of an actual or imminent start of a food intakeevent, a message/alert may be sent to the patient to remind him or herto take his/her medication. The medication could be insulin or othermedication, such as medications that need to be taken before, during, orafter eating a meal. In certain cases, it may be desirable to have adelay between the detection of an actual or imminent start of a foodintake event and the time when the alert is sent and this can be tunedas needed. For example, instead of sending an alert at the start of afood intake event, an alert may be sent after the system has detectedthat five, ten or some other certain number of bites or sips has beenconsumed. In another example, an alert might be sent one minute, fiveminutes or some other certain fixed time after the system has detectedan actual or imminent start of a food intake event. The timing of thealert may also depend at least in part on the confidence level of thepatient management system that an actual or imminent start of foodintake event has occurred. The alert might be sent when or some timeafter the patient management system has found conditions which,historically, preceded an eating event. Based on historical datarecorded by the patient management system from the patient's past eatingevents, the patient management system might be able to determine that anotification or reminder is needed ten minutes before a meal, at thestart of a meal, or during the meal. The patient management system mightalso be programmed to deal with “snooze” events, wherein the patient isnotified with an alert and the patient indicates that the patientmanagement system should resend the reminder at a defined time in thenear future, such as at a defined period of time after the initial alertor reminder or at a defined number of bites after the initial alert orreminder.

When the patient management system sends a message/alert to the patient,one or more persons or one or more systems may also be notified viaancillary messaging. The other person(s) or system(s) may be notified ofthe actual, probable or imminent start of a food intake event. If themessage/alert to the patient included a mechanism for the patient torespond, the other person(s) or system(s) may be informed of thepatient's response. The other person(s) or system(s) may also beinformed if the patient failed to respond. This might be helpful in thecase of parents and/or caregivers who support a patient.

In some cases, the patient management system might be programmed toaccept different inputs from the patient. For example, the patientmanagement system might have a user interface to accept from the patientan indication that a meal has concluded, that the meal will have zero,many, or some measure of carbohydrates.

As with the patient messaging, for this ancillary messaging, there maybe a delay between the detection of an actual or imminent start of afood intake event and the time when the alert is sent. Instead ofsending an alert at the start of a food intake event, an alert may besent after the system has detected that a certain number of bites orsips have been consumed. There may be a delay between sending the alertto the patient and notifying the other person(s) or system(s). If themessage/alert to the patient included a mechanism for the patient torespond, there may be a delay between the patient responding to themessage/alert and the other person(s) or system(s) being informed of theperson's response or failure to respond.

One or more other persons may be notified via a message that is sentover a cellular network. For example, a text message on his or her phoneor other mobile or wearable device.

An insulin therapy system will now be described.

The detection of an actual, probable or imminent start of a food intakeevent as described herein can be used to inform an insulin deliverysystem. Upon receiving a signal indicating an actual, probable orimminent start of a food intake event, the insulin delivery system maycalculate or estimate the adequate dosage of insulin to be administeredand the schedule for delivery of the insulin.

The insulin delivery system may use other parameters and inputs incalculating or estimating the dosing and frequency. For example, theinsulin delivery system may use current or prior glucose level readings,fluctuations in glucose level readings, parameters derived from glucoselevel readings or insulin-on-board (i.e., the insulin that wasadministered at an earlier time but is still active in the patient'sbody). Examples of parameters derived from glucose level readings couldbe the current slope (i.e. first derivative), the second or higher orderderivative of a glucose level reading, the maximum, mean, minimum valueof the glucose level readings in a certain time window preceding thecurrent food intake event, etc.

The insulin delivery system may also include parameters related to themeal activity itself, such as the duration of the meal, the pace ofeating, the amounts consumed. The insulin delivery system may also useother sensor inputs such as heart rate, blood pressure, bodytemperature, hydration level, fatigue level, etc. and can obtain thesefrom its own sensors or obtain some of them from other devices thepatient might be using for this or for other purposes.

The insulin delivery system may also include other inputs, such ascurrent or past physical activity levels, current or past sleep levels,and current or past stress levels. The insulin delivery system may alsoinclude specific personal information such as gender, age, height,weight, etc. The insulin delivery system may also include informationrelated to a patient's insulin needs. This could be information enteredor configured by the patient, by a caregiver or by a health record orhealthcare maintenance system. Information related to a patient'sinsulin needs may also be derived from historical data collected andstored by the insulin delivery system. For example, the amounts ofinsulin delivered by the insulin delivery system in a period of timepreceding the current food intake event.

In another embodiment, the insulin delivery system may take into accountthe amounts of insulin and delivery schedule associated with one or moreprior food intake events that occurred at or around the same time of dayand/or the same day of week, or within a defined time window around thecurrent time of day. An insulin delivery system may also take intoaccount a patient's current location.

Additional parameters related to the food intake event can also be usedto inform an insulin delivery system. An insulin delivery system may usesuch parameters to calculate or estimate an adequate dosage of insulinto be delivered and/or the schedule for the insulin delivery. Suchparameters may include, but are not limited to duration of eating ordrinking, amounts of food or drinks consumed, pace of eating, amount ofcarbohydrates consumed, eating method or type of utensils or containersused. Some of these additional parameters (e.g., duration or pace) maybe computed by the food intake tracking and feedback system withoutrequiring any user intervention. In other cases, a user intervention,input or confirmation by the user may be necessary.

An insulin delivery system may also use parameters related to past foodintake events to calculate or estimate the adequate insulin dosage andinsulin delivery schedule. The insulin delivery system may also includeparameters related to past food intake events, including parametersrelated to the time, location, duration, pace of eating or drinking,amounts consumed, eating method, utensils used, content consumed etc.Other parameters are also possible. For example, an insulin deliverysystem may take into account the duration of one or more past foodintake events and/or average pace of eating for one or more past foodintake events. In certain embodiments, the insulin delivery system mightonly consider parameters related to past food intake events thatoccurred within a specific time window prior to the current food intakeevent. In certain embodiments, the insulin delivery system might onlyconsider parameters from past food intake events that occurred at oraround the same time of day and/or the same day of week of the currentfood intake event. In certain embodiments, the insulin delivery systemmight only take into account parameters from one or more past foodintake events that occurred at or near the same location as the currentfood intake event.

In certain embodiments, the insulin delivery system may look at theimpact of past insulin dosages and delivery schedules on blood glucosereadings or on other sensor outputs that are used as a proxy for theblood glucose readings, such as for example the interstitial glucosereadings, to calculate or estimate the insulin dosage and determine thedelivery schedule of a current food intake event.

An insulin delivery system may continuously or periodically process itslogic and update the logic for insulin dosing and/or insulin deliveryscheduling accordingly based on one or more of the parameters describedabove.

The patient management system can be generalized beyond insulin, toadministration of a medication or substance that needs to beadministered in conjunction with a food intake event, especially if theamount of medication or other substance is related to parametersassociated with the food intake event.

A filter can be applied to determine if the meal is part of aqualified/applicable meal category.

The patient management system might be programmed to perform continuous,or periodic and frequent, evaluation during a meal and adjust theschedule or insulin amounts upwards or downwards based on observedchanges in blood glucose level readings (or proxies thereof, such asinterstitial glucose level readings). In one specific embodiment,insulin delivery may be suspended if a glucose level reading is below acertain threshold, or if a predictive algorithm embodied in executableprogram code executed by the patient management system outputs aprediction that a glucose reading will be below a certain level at afuture time if the current amounts and schedule are executed.

In this manner, the patient management system can send notifications andsend signals to an insulin delivery device, while taking into accountthe start of eating, the pace of eating, the anticipated end of eating,the duration of eating and other factors. For example, the patientmanagement system might instruct the insulin delivery device to deliverinsulin at the start of eating and during eating, using various inputs,such as heart rate, eating rate, body temperature, etc. to makeadjustments. In this manner, the patient management system can be partof a meal-aware, autonomous or semi-autonomous artificial pancreas.

Description of the Figures

FIG. 1 shows a high level functional diagram of a dietary tracking andfeedback system in accordance with an embodiment. A system for dietarytracking and feedback may in part include one or more of the following:a food intake event detection subsystem 101, one or more sensors 102, atracking and processing subsystem 103, a feedback subsystem 106, one ormore data storage units 104 and a learning subsystem 105 that mightperform non-real-time analysis. In some embodiments, elements shown inFIG. 1 are implemented in electronic hardware, while in others someelements are implemented in software and executed by a processor. Somefunctions might share hardware and processor/memory resources and somefunctions might be distributed. Functionality might be fully implementedin a sensor device such as wrist worn wearable device, or functionalitymight be implemented across the sensor device, a processing system thatthe sensor device communicates with, such as a smartphone, and/or aserver system that handles some functionality remote from the sensordevice.

For example, a wearable sensor device might make measurements andcommunicate them to a mobile device, which may process the data receivedfrom the wearable sensor device and use that information possiblycombined with other data inputs, to activate tracking and processingsubsystem 103. The tracking and processing subsystem 103 may beimplemented on the mobile device, on the wearable sensor device, or onanother electronic device. The tracking and processing subsystem 103 mayalso be distributed across multiple devices such as for example acrossthe mobile device and the wearable sensor device. The communicationmight be over the Internet to a server that further processes the data.Data or other information may be stored in a suitable format,distributed over multiple locations or centrally stored, in the formrecorded, or after some level of processing. Data may be storedtemporarily or permanently.

A first component of the system illustrated in FIG. 1 is the food intakeevent detection subsystem 101. A role of food intake event detectionsubsystem 101 is to identify the start and/or end of a food intake eventand communicate an actual, probable or imminent occurrence of an event.An event could, for example, be an event related to a specific activityor behavior. Other examples of an event that may be detected by eventdetection subsystem 101 could be an operator on a production line orelsewhere performing a specific task or executing a specific procedure.Yet another example could be a robot or robotic arm performing aspecific task or executing a specific procedure on a production arm orelsewhere.

In general, the device detects what could be the start of a food intakeevent or the probable start of a food intake event, but the device wouldwork sufficient for its purposes so long as the device reasonablydetermines such start/probable start. For clarity, that detection isreferred to as a “deemed start” of a food intake event and when variousprocesses, operations and elements are to perform some action orbehavior in connection with the start of a food intake event, it wouldbe acceptable for those various processes, operations and elements totake a deemed start as the start even if occasionally the deemed startis not in fact a start of a food intake event.

In one embodiment, the detection and/or signaling of the occurrence ofthe deemed start of a food intake event coincides with the deemed startof a food intake event. In another embodiment, it may occur sometimeafter the deemed start of the food intake event. In yet anotherembodiment, it may occur sometime before the deemed start of the foodintake event. It is usually desirable that the signaling is close to thedeemed start of the food intake event. In some embodiments of thecurrent disclosure, it may be beneficial that the detection and/orsignaling of the deemed start of a food intake event occurs ahead of thestart of said food intake event. This may for example be useful if amessage or signal is to be sent to the user, a healthcare provider orcaregiver ahead of the start of the food intake event as a coachingmechanism to help steer a user's food intake decisions or eating habits.

Methods for event detection may include, but are not limited to,detection based on monitoring of movement or position of the body or ofspecific parts of the body, monitoring of arm movement, position orgestures, monitoring of hand movement, position or gestures, monitoringof finger movement, position or gestures, monitoring of swallowingpatterns, monitoring of mouth and lips movement, monitoring of saliva,monitoring of movement of cheeks or jaws, monitoring of biting or teethgrinding, monitoring of sound and other signals from the mouth, thethroat and the digestive system. Methods for detection may includevisual, audio or any other types of sensory monitoring of the personand/or his or her surroundings.

The monitored signals may be generated by the dietary tracking andfeedback system. Alternatively, they may be generated by a separatesystem but be accessible to the dietary tracking and feedback systemthrough an interface. Machine learning and other data analyticstechniques may be applied to detect the start or probable start of afood intake event from the input signals being monitored.

In one example, the food intake detection system 101 may monitor theoutputs of accelerometer and/or gyroscope sensors to detect a possiblebite gesture or a possible sip gesture. Such gestures might bedetermined by a gesture processor that uses machine learning to distillgestures from sensor readings. The gesture processor might be part ofthe processor of the worn device or in another part of the system.

Gesture detection machine learning techniques as described elsewhereherein may be used to detect a bite gesture or sip gesture, but othertechniques are also possible. The food intake detection system 101 mayfurther assign a confidence level to the detected bite gesture or sipgesture. The confidence level corresponds to the likelihood that thedetected gesture is indeed a bite or sip gesture. The food intakedetection system may determine that the start of a food intake event hasoccurred based on the detection of a gesture and its confidence levelwithout any additional inputs. For example, the food intake eventdetection system 101 may decide that the start of a food intake eventhas occurred when the confidence level of the bite or sip gestureexceeds a pre-configured threshold.

Alternatively, when a possible bite or sip gesture has been detected,the food intake event detection system 101 may use additional inputs todetermine that the start or probable start of a food intake event hasoccurred. In one example, the food intake event detection system 101 maymonitor other gestures that are close in time to determine if the startof a food intake event has occurred. For example, upon detection of apossible bite gesture, the food intake event detection system 101 maywait for the detection of another bite gesture within a certain timewindow following the detection of the first gesture and/or with acertain confidence level before determining that the start of a foodintake event had occurred.

Upon such detection, the food intake detection system 101 may place oneor more circuits or components into a higher performance mode to furtherimprove the accuracy of the gesture detection. In another example, thefood intake event detection system 101 may take into consideration thetime of the day, or the location of the user to determine if the startor probable start of a food intake event has taken place. The foodintake event detection system may use machine learning or other dataanalytics techniques to improve the accuracy and reliability of itsdetection capabilities. For example, training data obtained from theuser and/or from other users at an earlier time may be used to train aclassifier. Training data may be obtained by asking for userconfirmation when a possible bite or sip gesture has been detected. Alabeled data record can then be created and stored in memory readable bythe gesture processor that includes the features related to the gesture,along with other contextual features, such as time of day or location. Aclassifier can then be trained on a labeled dataset comprised ofmultiple labeled data records set of labeled data records, and thetrained classifier model can then be used in a food intake eventdetection system to more accurately detect the start of a food intakeevent.

In another embodiment, the food intake detection subsystem may usetriggers to autonomously predict the probable start of a food intakeevent. Methods for autonomous detection of a probable start of a foodintake event based on triggers may include, but are not limited to,monitoring of a person's sleep patterns, monitoring of a person's stresslevel, monitoring of a person's activity level, monitoring of a person'slocation, monitoring of the people surrounding a person, monitoring of aperson's vital signs, monitoring of a person's hydration level,monitoring of a person's fatigue level. In some cases, the food intakedetection subsystem may monitor one or more specific trigger signals ortrigger events over a longer period of time and, in combination with thenon-real-time analysis and learning subsystem 105 apply machine learningor other data analytics techniques to predict the probable occurrence ofa start of a food intake event.

For example, without any additional information, it can be verydifficult to predict when a user will eat breakfast. However, if thesystem has a record over a number of days of the user's wake up time andthe day of the week, the system can use that historical pattern indetermining a likely time for the user to eat breakfast. Those recordsmight be determined by the system, possibly with feedback from the userabout their accuracy or those records might be determined by the userand input via a user interface of the system. The user interface mightbe the worn device itself or, for example, a smartphone app. As aresult, the system can process correlations in the historical data topredict the time or time window that the user is most likely to havebreakfast based on the current day of week and at what time the userwoke up. Other trigger signals or trigger events may also be used by thenon-real-time analysis and learning subsystem 105 to predict the timethat a user will eat breakfast.

In another example, the non-real-time analysis and learning system 105may, over a certain period of time record the stress level of a user.The stress level may, for example, be determined by monitoring andanalyzing the user's heart rate or certain parameters related to theuser's heart rate. The stress level may also be determined by analyzinga user's voice. The stress level may also be determined by analyzing thecontent of a user's messages or electronic communication. Other methodsfor determining the stress level are also possible. The non-real-timeanalysis and learning system 105 may furthermore, over the same periodof time, record the occurrence of food intake events and certaincharacteristics of the food intake event such as the pace of eating, thequantity of food consumed, the time spacing between food intake events,etc. It may then be possible by analyzing the historical data of stresslevels, the occurrence of food intake events and food intake eventcharacteristics and by looking at correlations in the historical data ofstress levels, the occurrence of food intake events and food intakeevent characteristics, to predict based on the current stress level theprobability that a user will start a food intake event in a certain timewindow in the future, or predict what time window in the future, theuser will be most likely to start a food intake event. It may also bepossible to predict characteristics of said food intake event, such asfor example pace of eating or quantity of consumption.

In specific embodiments, the non-real time analysis and learningsubsystem 105 may use historical data from different users, or acombination of data from other users and from the wearer, and usesimilarities between one or more of the different users and the wearer,such as age, gender, medical conditions, etc. to predict the probablestart of a food intake event by the wearer.

In yet other examples, the non-real-time analysis and learning subsystem105 may use methods similar to the methods described herein to predictwhen a user is most likely to relapse in a binge eating episode or ismost likely to start convenience snacking.

A variety of sensors may be used for such monitoring. The monitoredsignals may be generated by the dietary tracking and feedback system.Alternatively, they may be generated by a separate system but beaccessible to the dietary tracking and feedback system for processingand/or use as trigger signals. Machine learning and other data analyticstechniques may also be applied to predict some other characteristics ofthe probable intake event, such as the type and/or amount of food thatwill likely be consumed, the pace at which a person will likely beeating, the level of satisfaction a person will have from consuming thefood, etc.

The machine learning process performed as part of gesture recognitionmight use external data to further refine its decisions. This might bedone by non-real-time analysis and learning subsystem process. The dataanalytics process might, for example, consider the food intake eventsdetected by the gesture-sensing based food intake detection system andthe gesture-sensing based tracking and processing system, thus forming asecond layer of machine learning. For example, over a period of time,food intake events and characteristics related to those food intakeevents are recorded, such as eating pace, quantity of food consumption,food content, etc., while also tracking other parameters that are notdirectly, or perhaps not obviously, linked to the food intake event.This could be, for example, location information, time of day a personwakes up, stress level, certain patterns in a person's sleepingbehavior, calendar event details including time, event location andparticipant lists, phone call information including time, duration,phone number, etc., email meta-data such as time, duration, sender, etc.The data analytics process then identifies patterns and correlations.For example, it may determine a correlation between the number ofcalendar events during the day and the characteristics of the foodintake event(s) in the evening. This might be due to the user being morelikely to start snacking when arriving home, or that dinner is largerand/or more rushed when the number of calendar event(s) for that dayexceeds a certain threshold. With subsystem 105, it becomes possible topredict food intake events and characteristics from other signals andevents that are not obviously linked to food intake. Additionalcontextual metadata such as location, calendar information, day of weekor time of day may be used by the processing and analysis subsystem tomake such a determination or prediction.

Processing and analysis of one or more sensor inputs, and/or one or moreimages over longer periods of time, optionally using machine learning orother data analytics techniques may also be used to estimate theduration of a food intake event or may be used to predict that the endof a food intake event is probable or imminent.

In another embodiment, some user input 108 may be necessary or desirableto properly or more accurately detect the start and/or end of a foodintake event. Such user input may be provided in addition to externalinputs and inputs received from sensors 102. Alternatively, one or moreuser inputs may be used instead of any sensor inputs. User inputs mayinclude, but are not limited to activating a device, pressing a button,touching or moving a device or a specific portion of a device, taking apicture, issuing a voice command, making a selection on a screen orentering information using hardware and/or software that may include butis not limited to a keyboard, a touchscreen or voice-recognitiontechnology. If one or more user inputs are required, it is importantthat the user interaction is conceived and implemented in a way thatminimizes the negative impact on a person's normal activities or socialinteractions.

A food intake event detection subsystem 101 may combine multiple methodsto autonomously detect or predict the actual, probably or imminent startand/or end of a food intake event.

Another component of the system is the tracking and processing subsystem103. In a preferred embodiment of the present disclosure, this subsysteminterfaces 109 with the food intake event detection subsystem 101, andgets activated when it receives a signal from the food intake eventdetection subsystem 101 that the actual, probable or imminent start ofan event has been detected, and gets disabled when or sometime after itreceives a signal from the food intake event detection subsystem 101that the actual, probable or imminent ending of an event has beendetected. Upon detection of the start of a food intake event, the devicemight trigger activation of other sensors or components of the foodintake tracking system, and might also trigger the deactivation of thoseupon detection of the end of the food intake event.

Application: Food Logging

In existing food journaling methods, if a user forgets to make an entryor does not make an entry for another reason (intentionally orunintentionally), there is no record or history of the eating event.This leads to food diaries being incomplete, inaccurate and considerablyless useful for therapeutic purposes. Furthermore, content recorded inexisting food logging methods is typically limited to self-reporting ofcontent and amounts. There is no information about importantcharacteristics of how the food was consumed (e.g., pace, duration ofmeal).

The monitoring system shown in FIG. 1 may be used to automate or reducethe friction of food logging. In one embodiment of the presentdisclosure, event detection subsystem 101 uses information deduced frommotion sensors such as an accelerometer or gyroscope to detect andmonitor eating or drinking events from a subject's hand gestures. Inother embodiments, different sensor inputs may be used to deduce eatingor drinking events. Other sensors may include, but are not limited to,heart rate sensors, pressure sensors, proximity sensors, glucosesensors, optical sensors, image sensors, cameras, light meters,thermometers, ECG sensors and microphones.

Outputs of event detection subsystem 101 that indicate the occurrence ofa subject's eating or drinking events are recorded. Event detectionsubsystem 101 may do additional processing to obtain additional relevantinformation about the event such as start time, end time, metricsrepresentative of the subject's pace of eating or drinking, metricsrepresentative of quantities consumed. This additional information mayalso be recorded.

The detection of the occurrence of an eating or drinking event may berecorded as an entry in a food journal. Additional informationassociated with the eating or drinking event that can be obtained fromthe event detection subsystem may also be recorded as part of theconsumption event entry in the food journal. This can take the place ofmanually entering each eating event.

Information about eating or drinking events being recorded may includetime of event, duration of event, location of event, metrics related topace of consumption, metrics related to quantities consumed, eatingmethod(s), utensils used, etc.

Application: Medication Adherence

Since the event system is able to monitor events and gestures anddetermine consumption, this can be used to automatically monitor amedication administration protocol that defines when medication needs tobe taken and with what foods or other actions. This may be with specificmeal categories such breakfast, at certain times of day, etc. Amedication administration protocol might specify if a patient should eator drink with medication. Optionally, the medication administrationprotocol may also specify how much food or liquid a patient shouldconsume.

For example, when medication needs to be taken at certain times of day,a medication adherence system can monitor the time and, when it is timeto take medication, issue an alert. It may then also activate the eventdetection subsystem (if it is not yet already active) and startmonitoring the outputs of the event detection subsystem. It waits for anotification confirmation from user that he/she has taken themedication.

If a confirmation is received and if the medication administrationprotocol prescribes that medication needs to be taken with food orliquid, the medication adherence system monitors the outputs fromeating/drinking event detection subsystem and determines if rulesspecified by medication administration protocol are met. This could beas simple as confirming that an eating or drinking event has occurredwith or shortly after the intake of the medication. In case themedication administration protocol specifies that a minimum amount offood or fluid needs to be consumed, the medication adherence system maymonitor the additional outputs of the event detection subsystem (metricsrelated to quantities consumed) to confirm that this condition is met.Different rules/logic to determine if the medication administrationprotocol has been met is also possible.

If no confirmation is received, the medication adherence subsystem mayissue a second notification. Additional notifications are also possible.After a pre-configured number of notifications, the medication adherencesubsystem may issue an alert. Alerts may be issued to user as a textmessage, or may be sent to a remote server over the internet or over acellular connection (e.g., to hospital, caregiver).

If medication needs to be taken with specific meal categories, themedication adherence system may monitor the outputs of an eatingdetection subsystem. When an eating event is detected, it will use logicto determine the applicable meal category. If the meal category matchesthe category described by the medication administration protocol, itwill issue a notification to remind the user to take his/her medication.If the medication administration protocol prescribes that food or liquidshould be consumed with the medication, the monitoring logic outlinedabove may be implemented to determine that the medication administrationprotocol has been adhered to.

The medication adherence system may monitor the outputs of an eatingevent detection subsystem to determine if the start of an eating eventhas occurred and determine if the eating event is of the applicable mealcategory. When an eating event is detected and the meal category matchesthe category described by the medication administration protocol,certain actions might be taken, such as that it may activate an objectinformation retrieval system (e.g., NFC tags, imaging) to collect moreinformation about the objects the user is interacting with. In this way,it may obtain information about the medication from an NFC tag attachedto the pill box or medication container. In another use, it may verifythat the medication matches the medication prescribed by the medicationadministration protocol and/or issue a notification to remind the userto take his/her medication. If the medication administration protocolprescribes that food or liquid should be consumed with the medication,the monitoring logic outlined above may be implemented to determine thatthe medication administration protocol has been adhered to.

The system might also incorporate details about the medication obtainedfrom the object information collection system or through a differentmethod in the notification, record confirmations in response to thenotification from user that he/she has taken the medication, ask theuser for additional information about the medication, and/or send theuser a follow-on question at a pre-configured time after the user hastaken the medication to get additional inputs. (e.g., query about howthe user is feeling, query about pain levels, prompt to measure bloodsugar level).

Additional Embodiments

In another embodiment of the current disclosure, the tracking andprocessing subsystem may be activated and/or deactivated independent ofany signals from the food intake detection subsystem. It is alsopossible that certain parameters be tracked and/or processedindependently of any signals from the food intake detection subsystem,whereas the tracking and/or processing of other parameters may only beinitiated upon receiving a signal from the food intake event detectionsubsystem.

The sensor inputs may be the same or similar to the inputs sent to thefood intake event detection subsystem. Alternatively, different and/oradditional sensor inputs may be collected. Sensors may include, but arenot limited to, accelerometers, gyroscopes, magnetometers, imagesensors, cameras, optical sensors, proximity sensors, pressure sensors,odor sensors, gas sensors, Global Positioning Systems (GPS) circuit,microphones, galvanic skin response sensors, thermometers, ambient lightsensors, UV sensors, electrodes for electromyographic (“EMG”) potentialdetection, bio-impedance sensors, spectrometers, glucose sensors,touchscreen or capacitive sensors. Examples of sensor data includemotion data, temperature, heart rate, pulse, galvanic skin response,blood or body chemistry, audio or video recording and other sensor datadepending on the sensor type. The sensor inputs might be communicated toa processor wirelessly or via wires, in analog or digital form,intermediated by gating and/or clocking circuits or directly provided.

Processing methods used by the tracking and processing subsystem mayinclude, but are not limited to, data manipulation, algebraiccomputation, geo-tagging, statistical computing, machine learning,computer vision, speech recognition, pattern recognition, compressionand filtering.

Collected data may optionally be temporarily or permanently stored in adata storage unit. A tracking and processing subsystem may use aninterface to the data storage unit to place data or other information inthe data storage unit and to retrieve data or other information from thedata storage unit.

In a preferred embodiment of the present disclosure, the collection ofdata, processing and tracking happen autonomously and do not require anyspecial user intervention. Tracked parameters may include, but are notlimited to, the following: location, temperature of surroundings,ambient light, ambient sounds, biometric information, activity levels,image captures of food, food names and descriptions, portion sizes,fluid intake, caloric and nutrient information, counts of mouthfuls,bite counts, sip counts, time durations between consecutive bites orsips, and duration of food intake events. Tracked parameters may alsoinclude, for each bite or sip, the time duration that the user's hand,arm and/or utensil is near the user's mouth, the time duration that thecontent of the bite or sip resides in the user's mouth beforeswallowing. The methods may vary based on what sensor data is available.

In other embodiments of the present disclosure, some user interventionis required or may be desirable to achieve for example greater accuracyor input additional detail. User interventions may include, but are notlimited to, activating a device or specific functionality of a device,holding a device in position, taking pictures, adding voice annotations,recording video, making corrections or adjustments, providing feedback,doing data entry, taking measurements on food or on food samples.Measurements may include, but are not limited to, non-destructivetechniques such as for example obtaining one or more spectrographs offood items, or chemistry methods that may require a sample taken fromthe food.

The processing of sensor data and user inputs by the tracking andprocessing subsystem 103 usually occurs real-time or near real-time.There may be some delays, for example to conserve power or to workaround certain hardware limitations, but in some embodiments, theprocessing occurs during the food intake event, or in case of trackingoutside of a food intake event, around the time that the sensor or userinputs have been received.

In certain implementations or under certain circumstances, there may notbe real-time or near real-time access to the processing unit required toperform some or all of the processing. This may, for example, be due topower consumption or connectivity constraints. Other motivations orreasons are also possible. In that case, the inputs and/or partiallyprocessed data may be stored locally until a later time when access tothe processing unit becomes available.

In one specific embodiment of the present disclosure, sensor signalsthat track movement of a person's arm, hand or wrist may be sent to thetracking and processing subsystem 103. The tracking and processingsubsystem 103 may process and analyze such signals to identify that abite of food or sip of liquid has been consumed or has likely beenconsumed by said person. The tracking and processing subsystem 103 mayfurthermore process and analyze such signals to identify and/or quantifyother aspects of eating behavior such as for example the time separationbetween bites or sips, the speed of hand-to-mouth movement etc. Thetracking and processing subsystem 103 may furthermore process andanalyze such signals to identify certain aspects of the eating methodsuch as, for example, whether the person is eating with a fork or spoon,is drinking from a glass or can, or is consuming food without using anyutensils.

In a specific example, it might be that the wearer rotates his or herwrist in one direction when bringing an eating utensil or hand to themouth when taking a bite, but rotates in the other direction whensipping a liquid. The amount of rotation of a wearer's wrist as he orshe moves his or her wrist to the mouth or away from the mouth and theduration that the wrist is held at a higher rotation angle may also bedifferent for a drinking gesture versus an eating gesture. Other metricsmay be used to distinguish eating gestures from drinking gestures or todistinguish differences in eating methods. A combination of multiplemetrics may also be used. Other examples of metrics that may be used todistinguish eating gestures from drinking gestures or to distinguishdifferences in eating methods include but are not limited to the changein angle of the roll from the start or approximate start of the gestureuntil the time or approximate time that the hand reaches the mouth, thechange in angle of the roll from the time or approximate time that thehand is near the mouth until the end or approximate end of the gesture,the variance of accelerometer or gyroscope readings across one or moreof the axes for a duration of time when the hand is near the mouth, orfor a duration of time that is centered around when the hand is near themouth, or for a duration of time that may not be centered around whenthe hand is near the mouth but that includes the time when the hand isthe nearest to the mouth, the variance of the magnitude of theaccelerometer readings for a duration of time when the hand is near themouth, or for a duration of time that is centered around when the handis the nearest to the mouth, or for a duration of time that may not becentered around when the hand is the nearest to the mouth but thatincludes the time when the hand is the nearest to the mouth, the maximumvalue of the magnitude of the accelerometer readings for a duration oftime when the hand is near the mouth, or for a duration of time that iscentered around when the hand is the nearest to the mouth, or for aduration of time that may not be centered around when the hand is thenearest to the mouth but that includes the time when the hand is thenearest to the mouth. The magnitude of the accelerometer reading may bedefined as square root of the acceleration in each orthogonal direction(e.g., sense acceleration in the x, y, and z directions and calculateSQRT(ax²+ay²+az²)).

The position of the hand vis-à-vis the mouth can, for example, bedetermined by monitoring the pitch or the worn device and from there thepitch of the wearer's arm. The time corresponding to the peak of thepitch could be used as the moment in time when the hand is the nearestto the mouth. The time when the pitch starts rising could, for example,be used as the start time of the gesture. The time when the pitch stopsfalling could for example be used as the end time of the gesture.

Other definitions for nearest mouth position, start of movement and endof movement are also possible. For example, the time when the rollchanges direction could be used instead to determine the time when thearm or hand is the nearest to the mouth. The time when the roll stopschanging in a certain direction or at a certain speed could be usedinstead to determine the start time of the movement towards the mouth.

The tracking and processing subsystem may furthermore process andanalyze such signals to determine appropriate or preferred times toactivate other sensors. In one specific example, the tracking andprocessing subsystem may process and analyze such signals to determinean appropriate or preferred time to activate one or more cameras to takeone or more still or moving images of the food. By leveraging sensorsthat track arm, hand, finger or wrist movement and/or the orientationand position of the camera to activate the camera and/or automate theimage capture process, the complexity, capabilities and powerconsumption of the image-capture and image analysis system can begreatly reduced, and in certain cases better accuracy may be achieved.It also significantly reduces any privacy invasion concerns, as it nowbecomes possible to more precisely control the timing of image capturingand make it coincide with the cameras being focused on the food.

For example, the processor might analyze motion sensor inputs from anaccelerometer, a gyroscope, a magnetometer, etc., to identify theoptimal time to activate camera and capture picture and trigger thecamera at that time, perhaps based on when the processor determines thatthe view region of the camera encompasses the food to be photographed.In one example, the processor determines the start of an eating eventand signals the wearer to capture an image of the food being eaten andalso determines the end of the eating event and again signals the wearerto capture an image of what remains of the food or the plate, etc. Suchimages can be processed to determine consumption amounts and/or toconfirm consumption amounts already determined by the processor. In someembodiments, the image processing can be used as part of feedback totrain machine learning that the processor uses.

In some embodiments, the system may use sensors that track the movementof the wearer's arm or hand and only activate the camera when the systemdetermines from the movement sensing that the arm or hand are near themouth. In another example, the system may activate the camera sometimebetween the start of the movement towards the mouth and the time whenthe arm or hand is the nearest to the mouth. In yet another example, thesystem may activate the camera sometime between the time when the arm orhand is the nearest to the mouth and the end of the movement away fromthe mouth.

As mentioned above, the position of the hand vis-à-vis the mouth can bedetermined by monitoring the pitch and a rising pitch indicating a starttime of a movement towards the mouth and a falling pitch indicating anend time. Other definitions for nearest mouth position, start ofmovement and end of movement are also possible.

The position of the hand vis-à-vis the mouth can, for example, bedetermined by monitoring the pitch or the worn device and from there thepitch of the wearer's arm. The time corresponding to the peak of thepitch could be used as the moment in time when the hand is the nearestto the mouth. The time when the pitch starts rising could, for example,be used as the start time of the gesture. The time when the pitch stopsfalling could for example be used as the end time of the gesture.

The processing and analysis of sensor signals that track movement of auser's arm, hand or wrist may be combined with other methods such as theimage capture of food as it enters the mouth as proposed to build inredundancy and improve the robustness of a dietary tracking and feedbacksystem. For example, by processing and analysis of a user's arm, hand orwrist movement, information related to bite count and bite patternswould still be preserved, even if the camera were to be obscured ortampered with.

One or more of the sensor inputs may be still or streaming imagesobtained from one or more camera modules. Such images may require somelevel of processing and analysis. Processing and analysis methods may,among other methods, include one or more of the following methods:compression, deletion, resizing, filtering, image editing, and computervision techniques to identify objects such as, for example, specificfoods or dishes, or features such as, for example, portion sizes.

In addition to measuring bite counts and sip counts, the processor mightanalyze specifics, such as cadence and duration, to determine bite andsip sizes. Measuring the time that the wearer's hand, utensil or fluidcontainer was near their mouth might be used to derive a “near-mouth”duration that is in turn used as an input to generate an estimate sizeof the bite or sip. The amount of rotation of the wrist when sippingmight be useful for hydration tracking.

Measuring the amount of rotation of the wrist in one or more timesegments that are within the start and the end of the gesture may alsobe used to estimate the size of the bite or sip. For example, a systemmay measure the amount of rotation of the wrist from a time sometimeafter the start of the gesture to the time when the arm or hand is thenearest to the mouth. The time corresponding to the peak of the pitchcould be used as the moment in time when the hand is the nearest to themouth. The time when the pitch starts rising could for example be usedas the start time of the movement towards the mouth. The time when thepitch stops falling could for example be used as the end time of themovement away from the mouth. Other definitions for nearest mouthposition, start of movement and end of movement are also possible. Forexample, the time when the roll changes direction could be used insteadas the time when the arm or hand is the nearest to the mouth. The timewhen the roll stops changing in a certain direction or at a certainspeed could be used as the start time of the movement towards the mouth.One or more still or streaming images may be analyzed and/or compared bythe tracking and processing subsystem for one or multiple purposesincluding, but not limited to, the identification of food items, theidentification of food content, the identification or derivation ofnutritional information, the estimation of portion sizes and theinference of certain eating behaviors and eating patterns.

As one example, computer vision techniques, optionally combined withother image manipulation techniques may be used to identify foodcategories, specific food items and/or estimate portion sizes.Alternatively, images may be analyzed manually using a Mechanical Turkprocess or other crowdsourcing methods. Once the food categories and/orspecific food items have been identified, this information can be usedto retrieve nutritional information from one or more foods/nutritiondatabases.

As another example, information about a user's pace of eating ordrinking may be inferred from analyzing and comparing multiple imagescaptured at different times during the course of a food intake event. Asyet another example, images, optionally combined with other sensorinputs, may be used to distinguish a sit-down meal from finger foods orsnacks. As yet another example, the analysis of one image taken at thestart of a food intake event and another image taken at the end of afood intake event may provide information on the amount of food that wasactually consumed.

In a general case, sensor data is taken in by a processor that analyzesthat sensor data, possibly along with prior recorded data and/ormetadata about a person about whom the sensor data is sensing. Theprocessor performs computations, such as those described herein, toderive a sequence of sensed gestures. A sensed gesture might be one ofthe gestures described elsewhere herein, along with pertinent data aboutthe sensed gesture, such as the time of occurrence of the sensedgesture. The processor analyzes the sequence of sensed gestures todetermine the start of a behavior event, such as the starting of aneating event.

The determination of the start of an eating event may be based on asequence of sensed gestures, but it may also be based on the detectionof a single event (possibly with non-gesture based context). Forexample, if the system detects a bite gesture with a reasonably highconfidence level, the processor might consider that detection of thatindividual gesture to be the start of an eating event. The processor canalso analyze the sequence of sensed gestures to determine the end of thebehavior event. The determination of the end of an eating event may alsobe based on the absence of detected events. For example, if no bitegestures are detected in a given time period, the processor can assumethat the eating event ended.

Knowing the start and end of a behavior event allows the processor tomore accurately determine the gestures, since they are taken in contextand/or the processor may enable additional sensors or place one or moresensors or other components in a higher performance state, such as inexamples described elsewhere herein. Knowing the start and end of abehavior event also allows for power savings as, in some cases, it maybe possible to place the worn device in a lower power mode outsidecertain behavior events. Also, aggregation of individual gestures intoevents, possibly combined with prior recorded data about similarbehavior events from the same user or from other users in the past,allows the processor to derive meaningful characteristics about thebehavior event. For example, an eating pace during breakfast, lunch,dinner can be determined in this manner. As another example, if theprocessor has a state for a current behavior and that current behavioris teeth brushing, gestures that might appear to be eating or drinkinggestures would not be interpreted as eating or drinking gestures andthus not interpret sipping while teeth brushing as being consumption ofliquids. Behavior events might be general events (eating, walking,brushing teeth, etc.) or more specific (eating with a spoon, eating witha fork, drinking from a glass, drinking from a can, etc.).

While it might be possible to decode an indirect gesture, such asdetecting a pointing gesture and then determining the object that thesensed person is pointing at, of interest are gestures that themselvesare directly part of the event being detected. Some gestures areincidental gestures, such as gestures associated with operating thedevice, in which case incidental gestures might be excluded fromconsideration.

In a specific example, the system uses some set of sensors to determinethe start of an eating event with some confidence level and if theconfidence level is higher than a threshold, the system activatesadditional sensors. Thus, the accelerometer sensor might be used todetermine the start of an eating event with high confidence level, but agyroscope is put in a low power mode to conserve battery life. Theaccelerometer alone can detect a gesture that is indicative of aprobable bite or sip (e.g., an upward arm or hand movement or a hand orarm movement that is generally in the direction of the mouth), or agesture that is generally indicative of the start of an eating event.Upon detection of a first gesture that is generally indicative of apossible start of an eating event, the additional sensors (e.g.,gyroscope, etc.) may then be enabled. If a subsequent bite or sipgesture is detected, the processor determines that the start of aneating event had occurred and with a higher confidence level.

Event Detection

Knowing the start/end of a behavior event allows the processor to placeone or more sensor or other components in a higher performance state forthe duration of the behavior event. For example, when a start of abehavior event has been determined, the processor may increase thesampling rate of the accelerometer and/or gyroscope sensors used todetect gestures. As another example, when a start of a behavior eventhas been determined, the processor may increase the update rate at whichsensor data are sent to electronic device 219 for further processing toreduce latency.

Referring again to FIG. 1 , in addition to the tracking and processingsubsystem, the system of FIG. 1 may also include a non-real-timeanalysis and learning subsystem 105. The non-real-time analysis andlearning subsystem 105 can perform an analysis on larger datasets thattake a longer time to collect, such as historical data across multiplefood intake events and/or data from a larger population. Methods used bythe non-real-time analysis and learning subsystem 105 may include, butare not limited to, data manipulation, algebraic computation,geo-tagging, statistical computing, machine learning and data analytics,computer vision, speech recognition, pattern recognition, compressionand filtering.

Methods used by non-real-time analysis and learning subsystem 105 may,among other things, include data analytics on larger sets of datacollected over longer periods of time. As an example, one or more datainputs may be captured over a longer period of time and across multiplefood intake events to train a machine learning model. Such data inputsare hereafter referred to as training data sets. It is usually desirablethat the period of time over which a training data set is collected,hereafter referred to as the training period, is sufficiently long suchthat the collected data is representative of a person's typical foodintake.

A training data set may, among other things, include one or more of thefollowing food intake related information: number of bites per foodintake event, total bites count, duration of food intake event, pace offood intake or time between subsequent counts, categorization of foodintake content such as for example distinguishing solid foods fromliquids or sit-down meals from snacks or finger-foods. This informationmay be derived from one or more sensor inputs.

A training data set may furthermore include images of each or most itemsthat were consumed during each of the food intake events within thetraining period. The images may be processed using computer visionand/or other methods to identify food categories, specific food itemsand estimate portion sizes. This information may then in turn be used toquantify the number of calories and/or the macro-nutrient content of thefood items such as amounts of carbohydrates, fat, protein, etc.

In case the food was not consumed in its entirety, it may be desirableto take one picture of the food item at the start of the food intakeevent and one picture at the end of the food intake event to derive theportion of the food that was actually consumed. Other methods including,but not limited to, manual user input, may be used to add portion sizeinformation to the data in a training data set.

A training data set may furthermore include meta-data that do notdirectly quantify the food intake and/or eating behavior and patterns,but that may indirectly provide information, may correlate with foodintake events and/or eating behavior and/or may be triggers for theoccurrence of a food intake event or may influence eating habits,patterns and behavior. Such meta-data may, among other things, includeone or more of the following: gender, age, weight, social-economicstatus, timing information about the food intake event such as date,time of day, day of week, information about location of food intakeevent, vital signs information, hydration level information, and otherphysical, mental or environmental conditions such as for example hunger,stress, sleep, fatigue level, addiction, illness, social pressure, andexercise.

One or more training data sets may be used to train one or more machinelearning models which may then be used by one or more components of thedietary tracking and feedback systems to predict certain aspects of afood intake event and eating patterns and behaviors.

In one example, a model may be trained to predict the occurrence of afood intake event based on the tracking of one or more meta-data thatmay influence the occurrence of a food intake event. Othercharacteristics related to the probable food intake event, such as thetype and/or amount of food that will likely be consumed, the pace atwhich a person will likely be eating, the duration of the food intakeevent, and/or the level of satisfaction a person will have fromconsuming the food may also be predicted. Meta-data may, among otherthings, include one or more of the following: gender, age, weight,social-economic status, timing information about the food intake eventsuch as date, time of day, day of week, information about location offood intake event, vital signs information, hydration level information,and other physical, mental or environmental conditions such as forexample hunger, stress, sleep, fatigue level, addiction, illness, socialpressure, and exercise.

In another example, machine learning and data analytics may be appliedto derive metrics that may be used outside the training period toestimate caloric or other macro-nutrient intake, even if only limited orno food intake sensor inputs or images are available. Meta-data may beused to further tailor the value of such metrics based on additionalcontextual information. Meta-data may, among other things, include oneor more of the following: gender, age, weight, social-economic status,timing information about the food intake event such as date, time ofday, day of week, information about location of food intake event,information about generic food category, vital signs information,hydration level information, calendar events information, phone calllogs, email logs, and other physical, mental or environmental conditionssuch as for example hunger, stress, sleep, fatigue level, addiction,illness, social pressure, and exercise.

One example of such a metric would be “Calories per Bite”. By combiningthe bites count with the caloric information obtained from imageprocessing and analysis, a “Calories per bite” metric can be establishedfrom one or more training data sets. This metric can then be usedoutside the training period to estimate caloric intake based on bitescount only, even if no images or only limited images are available.

Another metric could be “Typical Bite Size”. By combining the bitescount with the portion size information obtained from image processingand analysis, a “Typical Bite size” metric can be established from oneor more training data sets. This metric can then be used outside thetraining period to estimate portion sizes based on bites count only,even if no images or only limited images are available. It may also beused to identify discrepancies between reported food intake and measuredfood intake based on bite count and typical bite size. A discrepancy mayindicate that a user is not reporting all the food items that he or sheis consuming. Or, alternatively, it may indicate that a user did notconsume all the food that he or she reported.

Bite actions might be determined by a processor reading accelerometerand gyroscope sensors, or more generally by reading motion sensors thatsense movement of body parts of the wearer. Then, by counting bites, atotal number of bites can be inferred. Also, the time sequence of thebites can be used by the processor do deduce an eating pattern.

Non-real-time analysis and learning subsystem 105 may also be usedtrack, analyze and help visualize larger sets of historical data, trackprogress against specific fixed or configured goals, and help establishsuch goals. It may furthermore be used to identify and track records,streaks and compare performance with that of friends or larger,optionally anonymous, populations.

Furthermore, in certain embodiments, non-real-time analysis and learningsubsystem 105 may among other data manipulation and processingtechniques, apply machine learning and data analytics techniques topredict the imminence of or the likelihood of developing certain healthissues, diseases and other medical conditions. In this case, trainingtypically requires historical food intake and/or eating behaviors datacaptured over longer periods of time and across a larger population. Itis furthermore desirable that training data sets include additionalmeta-data such as age, weight, gender, geographical information,socio-economic status, vital signs, medical records information,calendar information, phone call logs, email logs and/or otherinformation. Predictions may in turn be used to help steer healthoutcomes and/or prevent or delay the onset of certain diseases such as,for example, diabetes.

Non-real-time and learning subsystem 105 may also be used to learn andextract more information about other aspects including, but not limitedto, one or more of the following: a user's dietary and food preferences,a user's dining preferences, a user's restaurant preferences, and auser's food consumption. Such information may be used by the food intaketracking and feedback system to make specific recommendations to user.The food intake tracking and feedback system described in herein mayalso interface to or be integrated with other systems such as restaurantreservation systems online food or meal ordering systems, and others tofacilitate, streamline or automate the process of food or meal orderingor reservations.

Non-real-time and learning subsystem 105 may also be used to monitorfood intake over longer periods of times and detect any unusually longepisodes of no food intake activity. Such episodes may, among otherthings, indicate that the user stopped using the device, intentional orunintentional tampering with the device, a functional defect of thedevice or a medical situation such as for example a fall or death orloss of consciousness of the user. Detection of unusually long episodesof no food intake activity may be used to send a notification or alertto the user, one or more of his caregivers, a monitoring system, anemergency response system, or to a third party who may have a direct orindirect interest in being informed about the occurrence of suchepisodes.

Another component of the system shown in FIG. 1 is the feedbacksubsystem 106. The feedback subsystem 106 provides one or more feedbacksignals to the user or to any other person to which such feedbackinformation may be relevant. The feedback subsystem 106 may providereal-time or near real-time feedback related to a specific food intakeevent. Real-time or near real-time feedback generally refers to feedbackgiven around the time of a food intake event. This may include feedbackgiven during the food intake event, feedback given ahead of the start ofa food intake event and feedback given sometime after the end of a foodintake event. Alternatively, or additionally, the feedback subsystem mayprovide feedback to the user that is not directly linked to a specificfood intake event.

Feedback methods used by the feedback subsystem may include, but are notlimited to, haptic feedback whereby a haptic interface is used thatapplies forces, vibrations and/or motion to the user, audio feedbackwhere a speaker or any other audio interfaces may be used, or visualfeedback whereby a display, one or more LEDs and/or projected lightpatterns may be used. The feedback subsystem may use only one or acombination of more than one feedback method.

The feedback subsystem may be implemented in hardware, in software or ina combination of hardware and software. The feedback subsystem may beimplemented on the same device as the food intake event detectionsubsystem 101 and/or the tracking and processing subsystem 103.Alternatively, the feedback subsystem may be implemented in a devicethat is separate from the food intake event detection subsystem 101and/or the tracking and processing subsystem 103. The feedback subsystem106 may also be distributed across multiple devices, some of which mayoptionally house portions of some of the other subsystems illustrated inFIG. 1 .

In one embodiment, the feedback subsystem 106 may provide feedback tothe user to signal the actual, probable or imminent start of a foodintake event. The feedback subsystem 106 may also provide feedback tothe user during a food intake event to remind the user of the fact thata food intake event is taking place, to improve in-the-moment awarenessand/or to encourage mindful eating. The feedback subsystem may alsoprovide guidance on recommended portion sizes and/or food content, orprovide alternative suggestions to eating. Alternative suggestions maybe default suggestions or it may be custom suggestions that have beenprogrammed or configured by the user at a different time.

Feedback signals may include, but are not limited to, periodic hapticfeedback signals on a wearable device, sound alarms, display messages,or one or more notifications being pushed to his or her mobile phonedisplay.

Upon receiving a signal that indicates the start of a food intake event,or sometime thereafter, the user may confirm that a food intake event isindeed taking place. Confirmation can be used to for example triggerlogging of the event or may cause the system to prompt the user foradditional information.

In another embodiment of the present disclosure, the feedback subsysteminitiates feedback during a food intake event only if a certainthreshold of one or more of the parameters being tracked is reached. Asan example, if the time between subsequent bites or sips is beingtracked, feedback to the user may be initiated if the time, possiblyaveraged over a multiple bites or sips, is shorter than a fixed orprogrammed value to encourage the user to slow down. Similarly, feedbackmay be initiated if a fixed or programmed bites or sips count is beingexceeded.

In feedback subsystems where feedback is provided during a food intakeevent, the feedback provided by the feedback subsystem usually relatesto specifics of that particular food intake event. However, otherinformation including, but not limited to, information related to priorfood intake events, biometric information, mental health information,activity or fitness level information, and environmental information mayalso be provided by the feedback subsystem.

In yet another embodiment of the present disclosure, the feedbacksubsystem 106 may be sending one or more feedback signals outside aspecific food intake event. In one example of such an embodiment,ambient temperature and/or other parameters that may influence hydrationrequirements or otherwise directly or indirectly measure hydrationlevels may be tracked. Such tracking may happen continuously orperiodically, or otherwise independent from a specific food intakeevent. If one or more such parameters exceed a fixed or programmedthreshold, a feedback signal may be sent to, for example, encouragehim/her to take measures to improve hydration. The feedback subsystem106 might evaluate its inputs and determine that a preferred time forsending feedback is not during a food intake event, but after the foodintake event has ended. Some of the inputs to the feedback subsystem 106might be from a food intake event, but some might be from othermonitoring not directly measured as a result of the food intake event.

The decision to send a feedback signal may be independent of any foodintake tracking, such as in the embodiment described in the previousparagraph. Alternatively, such a decision may be linked to food intaketracking across one or multiple food intake events. For example, in oneembodiment of the current disclosure, the system described above couldbe modified to also track, either directly or indirectly, a person'sintake of fluids. For different ambient temperature ranges, saidembodiment could have pre-programmed fluid intake requirementthresholds. If for a measured ambient temperature, a person's intake offluids, possibly tracked and accumulated over a certain period of time,is not meeting the threshold for said ambient temperature, the systemmay issue a feedback signal to advise said person to increase his or herlevels of fluid intake.

Similarly, feedback signals or recommendations related to food intakemay among other parameters, be linked to tracking of activity levels,sleep levels, social context or circumstances, health or diseasediagnostics, and health or disease monitoring.

In yet another embodiment of the current disclosure, the feedbacksubsystem 106 may initiate a feedback signal when it has detected that afood intake event has started or is imminent or likely. In such anembodiment, feedback could for example be used as a cue to remind theuser log the food intake event or certain aspects of the food intakeevent that cannot be tracked automatically, or to influence or steer aperson's food intake behavior and/or the amount or content of the foodbeing consumed.

Information provided by the feedback subsystem 106 may include but isnot limited to information related to eating patterns or habits,information related to specific edible substances, such as for examplethe name, the description, the nutrient content, reviews, ratings and/orimages of food items or dishes, information related to triggers for foodintake, information related to triggers for eating patterns or habits,biometric or environmental information, or other information that may berelevant either directly or indirectly to a person's general food intakebehavior, health and/or wellness.

The feedback subsystem 106 may include the display of images of fooditems or dishes that have been consumed or may be consumed. Furthermore,the feedback subsystem 106 may include additional information on saidfood items or dishes, such as for example indication of how healthy theyare, nutrient content, backstories or preparation details, ratings,personalized feedback or other personalized information.

In certain embodiments of the current disclosure, the informationprovided by the feedback subsystem 106 may include non-real-timeinformation. The feedback subsystem 106 may for example include feedbackthat is based on processing and analysis of historical data and/or theprocessing and analysis of data that has been accumulated over a largerpopulation of users. The feedback subsystem 106 may further providefeedback that is independent of the tracking of any specific parameters.As an example, the feedback subsystem 106 may provide generic food,nutrition or health information or guidance.

In certain embodiments of the current disclosure, the user may interactwith the feedback subsystem 106 and provide inputs 116. For example, auser may suppress or customize certain or all feedback signals.

Non-real time feedback may, among other things, include historical data,overview of trends, personal records, streaks, performance against goalsor performance compared to friends or other people or groups of people,notifications of alarming trends, feedback from friends, social networksand social media, caregivers, nutritionists, physicians etc., coachingadvice and guidance.

Data or other information may be stored in data storage unit 104. It maybe stored in raw format. Alternatively, it may be stored after it hasbeen subject to some level of processing. Data may be stored temporarilyor permanently. Data or other information may be stored for a widevariety of reasons including, but not limited to, temporary storagewhile waiting for a processor or other system resources to becomeavailable, temporary storage to be combined with other data that may notbe available until a later time, storage to be fed back to the user inraw or processed format through the feedback subsystem 106, storage forlater consultation or review, storage for analysis for dietary and/orwellness coaching purposes, storage for statistical analysis across alarger population or on larger datasets, storage to perform patternrecognition methods or machine learning techniques on larger datasets.

The stored data and information, or portions thereof, may be accessibleto the user of the system. It is also possible that the stored data andinformation or portions thereof, may be shared with or can be accessedby a third party. Third parties may include, but are not limited to,friends, family members, caregivers, healthcare providers,nutritionists, wellness coaches, other users, companies that developand/or sell systems for dietary tracking and coaching, companies thatdevelop and/or sell components or subsystems for systems for dietarytracking and coaching, and insurance companies. In certaincircumstances, it may be desirable that data is made anonymous beforemaking it available to a third party.

FIG. 2 illustrates some of the components disposed in an electronicsystem used for dietary tracking and coaching, in accordance with oneembodiment of the present disclosure. The electronic system includes afirst electronic device 218, a second electronic device 219 (which maybe a mobile device), and a central processing and storage unit 220. Atypical system might have a calibration functionality, to allow forsensor and processor calibration.

Variations of the system shown in FIG. 2 are also possible and areincluded in the scope of the present disclosure. For example, in onevariation, electronic device 218 and electronic device 219 may becombined into a single electronic device. In another variation, thefunctionality of electronic device 218 may be distributed acrossmultiple devices. In some variations, a portion of the functionalityshown in FIG. 2 as being part of electronic device 218 may instead beincluded in electronic device 219. In some other variations, a portionof the functionality shown in FIG. 2 as being part of electronic device219 may instead be included in electronic device 218 and/or centralprocessing and storage unit 220. In yet another variation, the centralprocessing and storage unit 220 may not be present and all processingand storage may be done locally on electronic device 218 and/orelectronic device 219. Other variations are also possible.

An example of the electronic system of FIG. 2 is shown in FIG. 3 .Electronic device 218 may for example be a wearable device 321 that isworn around the wrist, arm or finger. Electronic device 218 may also beimplemented as a wearable patch that may be attached to the body or maybe embedded in clothing. Electronic device 218 may also be a module oradd-on device that can for example be attached to another wearabledevice, to jewelry, or to clothing. Electronic device 219 may forexample be a mobile device 322 such as a mobile phone, a tablet or asmart watch. Other embodiments of electronic device 219 and ofelectronic device 218 are also possible. The central processing andstorage unit 220 usually comprises of one or more computer systems orservers and one or more storage systems. The central processing andstorage unit 220 may for example be a remote datacenter 324 that isaccessible via the Internet using an Internet connection 325. Thecentral processing and storage unit 220 is often times shared amongand/or accessed by multiple users.

The wearable device 321 may communicate with mobile device 322 over awireless network. Wireless protocols used for communication over awireless network between wearable device 321 and mobile device 322 mayinclude, but is not limited to, Bluetooth, Bluetooth Smart (a.k.a.Bluetooth Low Energy), Bluetooth Mesh, ZigBee, Wi-Fi, Wi-Fi Direct, NFC,Cellular and Thread. A proprietary or wireless protocol, modificationsof a standardized wireless protocol or other standardized wirelessprotocols may also be used. In another embodiment of the currentdisclosure, the wearable device 321 and the mobile device 322 maycommunicate over a wired network.

The mobile device 322 may communicate wirelessly with a base station orAccess Point (“AP”) 323 that is connected to the Internet via Internetconnection 325. Via the Internet connection 325, mobile device 322 maytransfer data and information from wearable device 321 to one or morecentral processing and storage unit 220 that reside at a remotelocation, such as for example a remote data center. Via Internetconnection 325, mobile device 322 may also transfer data and informationfrom one or more central processing and storage unit 220 that reside ata remote location to wearable device 321. Other examples are alsopossible. In some embodiments, the central processing and storage unit220 may not be at a remote location, but may reside at the same locationor close to the wearable device 321 and/or mobile device 322. Wirelessprotocols used for communication between the mobile device 322 and thebase station or access point 323 may be the same as those between themobile device and the wearable device. A proprietary or wirelessprotocol, modifications of a standardized wireless protocol or otherstandardized wireless protocols may also be used.

The electronic system of FIG. 2 may also send data, information,notifications and/or instructions to and/or receive data, information,notifications and/or instructions from additional devices that areconnected to the Internet. Such devices could for example be a tablet,mobile phone, laptop or computer of one or more caregivers, members ofthe physician's office, coaches, family members, friends, people whomthe user has connected with on social media, or other people to whom theuser has given the authorization to share information. One example ofsuch a system is shown in FIG. 4 . In the example shown in FIG. 4 ,electronic device 441 is wirelessly connected to base station or AccessPoint 440 that is connected to the Internet via Internet connection 442.Examples of electronic device 441 may include, but are not limited to, atablet, mobile phone, laptop, computer, or smart watch. Via Internetconnection 442, electronic device 441 may receive data, instructions,notifications or other information from one or more central processingand storage units that may reside locally or at a remote location, suchas for example a remote data center. The communication capability caninclude Internet connection 442 or other communication channels.Electronic device 441 may also send information, instructions ornotifications to one or more computer servers or storage units 439.Central processing and storage unit 439 may forward this information,instructions or notifications to a mobile device 436 via the Internet438 and the base station or Access Point (“AP”) 437.

Other examples are also possible. In some embodiments, the centralprocessing and storage unit 439 may not be at a remote location, but mayreside at the same location or close to the wearable device 435 and/ormobile device 436. FIG. 4 shows electronic device 441 as beingwirelessly connected to the base station or Access Point. A wiredconnection between electronic device 441 and a router that connects tothe Internet via an Internet connection 442 is also possible.

FIG. 5 illustrates another embodiment of the present disclosure. In FIG.5 , a wearable device 543 can exchange data or other informationdirectly with a central processing and storage system 546 via a basestation or Access Point 544 and the Internet without having to gothrough a mobile device 545. Mobile device 545 may exchange data orother information with wearable device 543 either via central processingand storage system 546 or via a local wireless or wired network. Thecentral processing and storage system 546 may exchange information withone or more additional electronic devices 550.

FIG. 6 illustrates some of the components disposed in electronic device218, in accordance with one embodiment. Electronic device 218 typicallyincludes, in part, one or more sensor units 627, a processing unit 628,memory 629 that can include or be realized as computer-readable storagemedia having program code instructions, a clock or crystal 630, radiocircuitry 634, and a power management unit (“PMU”) 631. Electronicdevice 218 may also include one or more camera modules 626, one or morestimulus units 633 and one or more user interfaces 632. Although notshown, other components like capacitors, resistors, inductors may alsobe included in said electronic device 218. Power Management unit 631may, among other things, include one or more of the following: battery,charging circuitry, regulators, hardware to disable the power to one ormore components, power plug.

In many embodiments, electronic device 218 is a size constrained,power-sensitive battery operated device with a simple and limited userinterface. Where power is limited, electronic device 218 might beprogrammed to save power outside of behavior events. For example, aprocessor in electronic device 218 might be programmed to determine thestart of a behavior event, such as an eating event, and then power upadditional sensors, place certain sensors in a higher performance modeand/or perform additional computations until the processor determines anend of the behavior event, at which point the processor might turn offthe additional sensors, place certain sensors back in a lowerperformance mode and omit the additional computations.

For example, the processor might be programmed to disable allmotion-detection related circuitry, with exception of an accelerometer.The processor could then monitor accelerometer sensor data and if thosedata indicate an actual or prominent food intake activity such as a biteor sip gesture, then the processor could activate additional circuitry,such as a data recording mechanism. The processor might use theaccelerometer sensor data to monitor a pitch of the wearer's arm.

For example, the processor might measure pitch of the wearer's arm untilthe pitch exceeds a certain threshold, perhaps one indicative of a handor arm movement towards the wearers' mouth. Once that is detected, theprocessor can change the state (such as by changing a memory locationset aside for this state from “inactive” or “out-of-event” to “in anaction” or “in-event”) and activate additional circuitry or activate ahigher performance mode of specific circuitry or components. In anotherembodiment, other accelerometer sensor data characteristics such asfirst integral of acceleration (velocity) or the second integral ofacceleration (distance traveled), or characteristics related to orderived from the first and/or second integral of acceleration might beused, as determined from one or more accelerometer axis. A machinelearning process might be used to detect specific movements andtranslate those to gestures.

An end of a food intake event might be detected by the processor byconsidering whether a certain time has expired since a last bite or sipmovement or when other data (metadata about the wearer, motion-detectionsensor data, and/or historical data of the wearer, or a combination ofthose). Based on those, the processor makes a determination that a foodintake event is not likely and then changes the state of the electronicdevice to an inactive monitoring state, possibly a lower power mode.

The lower power mode might be implemented by the processor reducing thesampling rate of the accelerometer and/or gyroscope, powering down thegyroscope, reducing the update rate at which sensor data is transferredfrom the electronic device (such as electronic device 218) to thesupport device (such as electronic device 219), compressing the databefore transferring the data from the sensing electronic device to thesupport electronic device.

In some embodiments of the present disclosure, some of the componentsthat are shown in FIG. 5 as separate components may be combined. As anexample, the processing unit, memory, radio circuitry and PMUfunctionality may entirely or in part be combined in a single wirelessmicrocontroller unit (“MCU”). Other combinations are also possible.Similarly, components that are shown as a single component in FIG. 5 maybe implemented as multiple components. As an example, the processingfunctionality may be distributed across multiple processors. Likewise,data storage functionality may be distributed across multiple memorycomponents. Other examples of distributed implementations are alsopossible.

In another embodiment of the present disclosure, the radio circuitry maynot be present and instead a different interface (such as for example aUSB interface and cable) may be used to transfer data or information toand/or from the electronic device 218.

Stimulus unit 633 may provide feedback to the user of the electronicdevice. A stimulus unit 633 may include but is not limited to a hapticinterface that applies forces, vibrations or motions to the user, aspeaker or headphones interface that provides sounds to the user, and adisplay that provides visual feedback to the user.

In certain embodiments, the processing and analysis of signals fromsensors embedded in electronic device 218 can detect when electronicdevice has been disabled, tampered with, removed from the body or is notbeing used. This can be used to conserve power, or to send anotification to the user, a friend or another person who might directlyor indirectly have an interest in being notified if electronic device218 is not being used properly.

Description Detection/Prediction of Start/End of Food Intake Event

In a preferred embodiment, the electronic device 218 is worn around thewrist, arm or finger and has one or more sensors that generate datanecessary to detect the start and/or end of a food intake event. Theelectronic device 218 may also be integrated in a patch that can beattached to a person's arm or wrist. The electronic device 218 may alsobe a module or add-on device that can be attached to another device thatis worn around the wrist, arm or finger. Sensors used to detect thestart and/or end of a food intake event may, among other sensors,include one or more of the sensors described herein.

The raw sensor outputs may be stored locally in memory 629 and processedlocally on processing unit 628 to detect if the start or end of a foodintake event has occurred. Alternatively, one or more sensor outputs maybe sent to electronic device 219 and/or the central processing andstorage unit 220, either in raw or processed format, for furtherprocessing and to detect if the start or end of a food intake event hasoccurred. Regardless of where the processing for food intake detectionoccurs, sensor outputs in raw or processed format may be stored insideelectronic device 218, inside electronic device 219 and/or inside thecentral processing and storage unit 220.

The sensor or sensors that generate data necessary for the detection ofthe start and/or end of a food intake event may be internal toelectronic device 218. Alternatively, one or more of the sensorsresponsible for the detection of the start of a food intake event may beexternal to electronic device 218, but are able to relay relevantinformation to the electronic device 218 either directly through directwireless or wired, communication with electronic device 218 orindirectly, through another device. It is also possible that electronicdevice 218 and the external sensor or sensors are able to relayinformation to electronic device 219, but are not able to relayinformation to one another directly.

In case of indirect communication through another device such as amobile phone or other portable or stationary device, such third deviceis able to receive data or information from one or more external sensorunits, optionally processes such data or information, and forwardseither the raw or processed data or information to electronic device218. The communication to and from the electronic device 218 may bewired or wireless, or a combination of both.

Examples of sensors that may be external to electronic device 218 may beone or more sensors embedded in a necklace or pendant worn around theneck, one or more sensors embedded in patches that are attached to adifferent location on the body, one or more sensors embedded in asupplemental second wearable device that is worn around the other arm orwrist or on a finger of the other hand, or one or more sensorsintegrated in a tooth. In some embodiments, the electronic device isworn on one hand or arm but detects movement of the other hand or arm.In some embodiments, electronic devices are worn on each hand.

Information obtained from the non-real-time analysis and learningsubsystem 105 may also be used, optionally in combination withinformation from one or more sensors 627, to predict or facilitate thedetection of a probable, imminent or actual start/end of a food intakeevent.

It is often desirable that the detection and/or the prediction of thestart and/or end of a food intake event happens autonomously withoutrequiring user intervention. For example, if the actual, probable orimminent start of a food intake event is predicted or detectedautonomously, this information can be used as a trigger to activate orpower up specific components or circuits that are only needed during afood intake event. This can help conserve power and extend the batterylife of electronic device 218. The prediction or detection of an actual,probable or imminent start of a food intake event can also be used toissue a cue or reminder to the user. A cue can for example be sent tothe user to remind him/her to take further actions including, but notlimited to, logging the food intake event or taking a picture of thefood. Upon detection of the start of a food intake event, one or morecues, possibly spread out over the duration of the food intake event, toremind the user that a food intake event is taking place and improvingin-the-moment awareness and/or encourage mindful eating. Cues orreminders may for example be sent through discrete haptic feedback usingone or more stimulus units 633. Other methods using one or more userinterfaces 632, such as for example one or more LEDs, a display message,or an audio signal, are also possible. Alternatively, a mobile devicemay be used to communicate cues, reminders or other information such asfor example portion size recommendations or alternative suggestions toeating to the user.

If the actual, probable or imminent end of a food intake event ispredicted or detected autonomously, this information can be used as atrigger to power down or at least put in a lower power mode one or morecircuits or components of an electronic device that are only neededduring a food intake event. This can help conserve power and extend thebattery life of the electronic device. The detection of the actual,probable or imminent end of a food intake event may also be used tomodify or suspend the feedback provided to the user by one or morestimulus units 633, by one or more of the user interfaces 632, and/or bythe mobile device.

In some embodiments of the present disclosure, the detection orprediction of the actual, probable or imminent start and/or end of afood intake event may not be entirely autonomously. For example, theuser may be required to make a specific arm, wrist, hand or fingergesture to signal to the electronic device the actual, probable orimminent start and/or end of a food intake event. The arm, wrist, handor finger gesture is then detected by one or more sensors inside theelectronic device. It is usually desirable that the arm, wrist, hand orfinger gesture or gestures required to indicate the start and/or end ofa food intake event can be performed in a subtle and discrete way. Othermethods may also be used. For example, the user may be asked to push abutton on the electronic device to indicate the start and/or end of afood intake event. Voice activation commands using a microphone that isbuilt into the electronic device may also be used. Other methods arealso possible.

Description of Tracking of Eating Behaviors and Patterns

In a particular embodiment, the electronic device is worn around thewrist, arm or finger and has one or more sensors that generate data thatfacilitate the measurement and analysis of eating behaviors, patternsand habits. Sensors used for measuring and analyzing certain eatingbehaviors and patterns may include one or more of the sensors describedherein.

Relevant metrics that may be used to quantify and track eating behaviorsand eating patterns may include, but are not limited to, the timebetween subsequent bites or sips, the distance between the plate and theuser's mouth, the speed of arm movement towards and/or away from theuser's mouth, and the number of bites or sips during a single foodintake event, derived from the total count of arm movementscorresponding to a bite or sip, specific chewing behavior andcharacteristics, the time between taking a bite and swallowing, amountof chewing prior to swallowing.

FIG. 6 illustrates examples of components of such an electronic device.As illustrated, the raw sensor outputs may be stored locally in a memory629 and processed locally on a processing unit 628. Alternatively, oneor more sensor outputs may be sent to the electronic device and/or aprocessing unit 628, either in raw or in processed format, for furtherprocessing and analysis. Regardless of where the processing and analysisof eating behaviors and patterns occurs, sensor outputs in raw orprocessed format may be stored inside the electronic device, inside anancillary electronic device, such as a mobile phone, and/or inside theprocessing unit 628.

In some embodiments, the generation, collection and/or processing ofdata that facilitate the measurement and analysis of eating behaviors,patterns and habits may be continuously, periodically or otherwiseindependently of the start and/or end of a food intake event.Alternatively, the generation, collection and/or processing of data thatfacilitate the measurement and analysis of eating behavior and patternsmay occur only during a food intake event or be otherwise linked to aspecific food intake event. It is also possible that some sensor dataare being generated, collected and/or processed continuously,periodically or otherwise independently of the start and/or end of afood intake event whereas other sensor data are taken during a foodintake event or otherwise linked to a food intake event.

The sensor or sensors that generate data necessary for measuring andanalyzing eating behaviors and eating patterns may be internal to theelectronic device. Alternatively, one or more of the sensors thatgenerate data necessary for measuring and analyzing eating behaviors andeating patterns may be external to the electronic device, but are ableto relay relevant information to the electronic device either directlythrough direct wireless or wired, communication with the electronicdevice or indirectly, through another device.

In case of indirect communication through another device such as amobile phone or other portable or stationary device, such third deviceis able to receive data or information from the external sensor unit,optionally processes such data or information, and forwards either theraw or processed data or information to the tracking device. Thecommunication to and from the electronic device may be wired orwireless, or a combination of both.

Examples of sensors that may be external to the electronic device may beone or more sensors embedded in a necklace or pendant worn around theneck, one or more sensors embedded in patches that are attached to adifferent location on the body, one or more sensors embedded in asupplemental second wearable device that is worn around the other arm orwrist or on a finger of the other hand, or one or more sensorsintegrated in a tooth.

Description of Use of Camera Module and Image Capture

While use of a camera to capture images of food have been proposed inthe prior art, they typically rely on the user taking pictures with hisor her mobile phone or tablet. Unfortunately, image capture using amobile phone or tablet imposes significant friction of use, may not besocially acceptable in certain dining situations or may interfere withthe authenticity of the dining experience. It is often times notdesirable or inappropriate that the user needs to pull out his or hermobile phone, unlock the screen, open a mobile app and take a pictureusing the camera that is built into the mobile phone.

If user intervention is required, it is generally desirable that theuser intervention can be performed in a subtle and discrete manner andwith as little friction as possible. In order to minimize the frictionof use, it is often times desirable that the image capture can beinitiated from the electronic device directly.

While the examples provided herein use image capture of food and mealscenarios as examples, upon reading this disclosure, it should be clearthat the methods and apparatus described herein can be applied to imagecapture of objects and scenes other than foods and meal scenarios. Forexample, a viewfinder-less camera can have application outside of thefood event capture domain.

In some embodiments, the electronic device is worn around the wrist, armor finger and includes one or more camera modules 626. One or morecamera modules 626 may be used for the capture of still images inaccordance with one embodiment of the present disclosure, and for thecapture of one or more video streams in accordance with anotherembodiment of the present disclosure. In yet another embodiment of thepresent disclosure, a combination of still and streaming images is alsopossible.

One or more camera modules may also be included in a device that is wornat a different location around the body, such as a necklace or pendantthat is worn around the neck, or a device that is attached to orintegrated with the user's clothing, with the camera or camera modulespreferably aiming towards the front so that it can be in line of sightwith the food being consumed.

In some embodiments, activation of a camera module and/or image captureby a camera module may require some level of user intervention. Userintervention may, among other things, include pressing a button, issuinga voice command into a microphone that is built into the electronicdevice or the mobile device, making a selection using a displayintegrated in the electronic device or the mobile device, issuing aspecific arm, wrist, hand or finger gesture, directing the camera sothat the object of interest is within view of the camera, removingobstacles that may be in the line of sight between the camera and theobject of interest, and/or adjusting the position of the object ofinterest so that it is within view of the camera. Other userintervention methods, or a combination of multiple user interventionmethods are also possible.

In one embodiment of the present disclosure, a camera module is builtinto an electronic device, such as a wearable device, that may not havea viewfinder, or may not have a display that can give feedback to theuser about the area that is within view of the camera. In this case, theelectronic device may include a light source that projects a pattern ofvisible light onto a surface or onto an object to indicate to the userthe area that is within the view of the camera. One or more LightEmitting Diodes (LEDs) may be used as the light source. Other lightsources including, but not limited to, laser, halogen or incandescentlight sources are also possible. The pattern of visible light may, amongother things, be used by the user to adjust the position of the camera,adjust the position the object of interest and/or remove any objectsthat are obstructing the line of sight between the object of interestand the camera.

The light source may also be used to communicate other information tothe user. As an example, the electronic device may use inputs from oneor more proximity sensors, process those inputs to determine if thecamera is within the proper distance range from the object of interest,and use one or more light sources to communicate to the user that thecamera is within the proper distance range, that the user needs toincrease the distance between camera and the object of interest, or thatthe user needs to reduce the distance between the camera and the objectof interest.

The light source may also be used in combination with an ambient lightsensor to communicate to the user if the ambient light is insufficientor too strong for an adequate quality image capture.

The light source may also be used to communicate information including,but not limited, to a low battery situation or a functional defect.

The light source may also be used to communicate dietary coachinginformation. As an example, the light source might, among other things,indicate if not enough or too much time has expired since the previousfood intake event, or may communicate to the user how he/she is doingagainst specific dietary goals.

Signaling mechanisms to convey specific messages using one or more lightsources may include, but are not limited to, one or more of thefollowing: specific light intensities or light intensity patterns,specific light colors or light color patterns, specific spatial ortemporal light patterns. Multiple mechanisms may also be combined tosignal one specific message.

In another embodiment of the current disclosure, a camera module may bebuilt into an electronic device, such as a wearable device, that doesnot have a viewfinder or does not have a display that can give feedbackto the user about the area that is within view of the camera. Instead ofor in addition to using a light source, one or more images captured bythe camera module, possibly combined with inputs from other sensors thatare embedded in the electronic device may be sent to the processing unitinside the electronic device, the processing unit inside the mobiledevice, and/or the processing unit 628 for analysis and to determine ifthe object of interest is within proper view and/or proper focal rangeof the camera. The results of the analysis may be communicated to theuser using one of the feedback mechanisms available in the electronicdevice including, but not limited to, haptic feedback, visual feedbackusing one or more LEDs or a display, and/or audio feedback.

In some other embodiments of the present disclosure, the electronicdevice may capture one or more images without any user intervention. Theelectronic device may continuously, periodically or otherwiseindependently of any food intake event capture still or streamingimages. Alternatively, the electronic device may only activate one ormore of its camera modules around or during the time of a food intakeevent. As an example, an electronic device may only activate one or moreof its camera modules and capture one or more images after the start ofa food intake event has been detected and before the end of a foodintake event has been detected. It may use one or more of its cameramodules to capture one of more images of food items or dishes in theirentirety, or of a portion of one or more food items or dishes.

In some embodiments, one camera may be used to capture one or moreimages of food items that are on a plate, table or other stationarysurface, and a second camera may be used to capture one or more imagesof food items that are being held by the user, such as for examplefinger foods or drinks. The use of more than one camera may be desirablein situations where no user intervention is desirable and the position,area of view or focal range of a single camera is not suite to captureall possible meal scenarios.

In one example embodiment, the position, the orientation and the angleof view of the camera are such that an image or video capture ispossible without any user intervention. In such an embodiment, thewearable device may use a variety of techniques to determine the propertiming of the image or video stream capture such that it can capture thefood or a portion of the food being consumed. It may also choose tocapture multiple images or video streams for this purpose. Techniques todetermine the proper timing may include, but are not limited to, thefollowing: sensing of proximity, sensing of acceleration or motion (orabsence thereof), location information. Such sensor information may beused by itself or in combination with pattern recognition or dataanalytics techniques (or a combination of both) to predict the besttiming for the image or video capture. Techniques may include, but arenot limited to, training of a model based on machine learning.

The captured still and/or streaming images usually require some level ofprocessing. Processing may include but is not limited to compression,deletion, resizing, filtering, image editing, and computer visiontechniques to identify objects such as for example specific foods ordishes, or features such as for example portion sizes. Processing unitsthat may be used to process still or streaming images from the cameramodule or modules, regardless of whether or not the camera module ormodules are internal to the electronic device, include, but are notlimited to, the processing unit inside the electronic device, theprocessing unit inside a mobile device and/or a processing unit 628which may reside at the same location as where the electronic device isbeing used or alternatively, may reside at a remote location (e.g., in acloud server) in which case it may be accessed via the internet. Theimage processing may also be distributed among a combination of theabovementioned processing units.

Examples of local processing may include but are not limited to:selection of one or more still images out of multiple images or one ormore video streams, compression of images or video stream, applicationof computer vision algorithms on one or more images or video streams.

Local processing may include compression. In case of compression, acompressed image may be transmitted as part of a time criticaltransaction whereas its non-compressed version may be saved fortransmission at a later time.

One or more still or streaming images may be analyzed and/or comparedfor one or multiple purposes including, but not limited to, thedetection of the start and/or end of a food intake event, theidentification of food items, the identification of food content, theidentification or derivation of nutritional information, the estimationof portion sizes and the inference of certain eating behaviors andeating patterns.

As one example, computer vision techniques, optionally combined withother image manipulation techniques may be used to identify foodcategories, specific food items and/or estimate portion sizes.Alternatively, images may be analyzed manually using a Mechanical Turkprocess or other crowdsourcing methods. Once the food categories and/orspecific food items have been identified, this information can be usedto retrieve nutritional information from one or more foods/nutritiondatabases.

As another example, information about a user's pace of eating ordrinking may be inferred from analyzing and comparing multiple imagescaptured at different times during the course of a food intake event. Asyet another example, images, optionally combined with other sensorinformation, may be used to distinguish a sit-down meal from fingerfoods or snacks. As yet another example, the analysis of one image takenat the start of a food intake event and another image taken at the endof a food intake event may provide information on the amount of foodthat was actually consumed.

Description of User Feedback

In a preferred embodiment of the present disclosure, the electronicdevice 218 is worn around the wrist, arm or finger and has one or morestimulus units and/or user interfaces that allow for feedback to theuser or the wearer of the electronic device. In a different embodimentof the present disclosure, electronic device 218 may be implemented as awearable patch that may be attached to the body or may be embedded inclothing.

Feedback usually includes feedback that is food or food intake related.Feedback methods may include, but are not limited to, haptic feedback,visual feedback using LEDs or a display or audio feedback. In one suchembodiment, electronic device 218 may have a haptic interface thatvibrates once or multiple times when the start and/or end of a foodintake event have been detected. In another embodiment, electronicdevice 218 may have a haptic interface that vibrates once or multipletimes when the tracking and processing subsystem identifies that thewearer of the device is consuming food and is showing eating behaviorthat is exceeding certain programmed thresholds, such as for exampleeating too fast, too slow or too much. Alternatively, the hapticinterface may vibrate one or more times during a food intake event,independent of any specific eating behavior, for example to remind thewearer of the fact that a food intake event is taking place and/or toimprove in-the-moment awareness and to encourage mindful eating. Otherfeedback methods are also possible, and different metrics or criteriamay be used to trigger an activation of such feedback methods.

In a different embodiment of the present disclosure, feedback isprovided to the user through a device that is separate from theelectronic device 218. One or more stimulus units and/or user interfacesrequired to provide feedback to the user may be external to electronicdevice 218. As an example, one or more stimulus units and/or userinterfaces may be inside electronic device 219, and one or more of saidstimulus units and/or user interfaces inside electronic device 219 maybe used to provide feedback instead of or in addition to feedbackprovided by electronic device 218. Examples may include, but are notlimited to, messages being shown on the display of electronic device219, or sound alarms being issued by the audio subsystem embedded insideelectronic device 219.

Alternatively, feedback may be provided through a device that isseparate from both electronic device 218 and electronic device 219, butthat is able to at a minimum, either directly or indirectly, receivedata from at least one of those devices.

In addition to or instead of feedback provided around or during the timeof a food intake event, the system of FIG. 2 or FIG. 3 may also providefeedback that may span multiple food intake events or may not be linkedto a specific food intake event or set of food intake events. Examplesof such feedback may include, but are not limited to, food content andnutritional information, historical data summaries, overviews of one ormore tracked parameters over an extended period of time, progress of oneor more tracked parameters, personalized dietary coaching and advice,benchmarking of one or more tracked parameters against peers or otherusers with similar profile.

Detailed Description of Specific Embodiments

In one specific embodiment of the present disclosure, electronic device218 is a wearable device in the form factor of a bracelet or wristbandthat is worn around the wrist or arm of a user's dominant hand.Electronic device 219 is a mobile phone and central processing andstorage unit 220 is one or more computer servers and data storage thatare located at a remote location.

One possible implementation of a wearable bracelet or wristband inaccordance with aspects of the present invention is shown in FIG. 7 .Wearable device 770 may optionally be implemented using a modulardesign, wherein individual modules include one or more subsets of thecomponents and overall functionality. The user may choose to addspecific modules based on his personal preferences and requirements.

The wearable device 770 may include a processor, a program code memoryor storage medium, and program code (software) stored therein and/orinside electronic device 219 to optionally allow users to customize asubset of the functionality of wearable device 770.

Wearable device 770 relies on battery 769 and Power Management Unit(“PMU”) 760 to deliver power at the proper supply voltage levels to allelectronic circuits and components. Power Management Unit 760 may alsoinclude battery-recharging circuitry. Power Management Unit 760 may alsoinclude hardware such as switches that allows power to specificelectronics circuits and components to be cut off when not in use.

When there is no behavior event in progress, most circuitry andcomponents in wearable device 770 are switched off to conserve power.Only circuitry and components that are required to detect or helppredict the start of a behavior event may remain enabled. For example,if no motion is being detected, all sensor circuits but theaccelerometer may be switched off and the accelerometer may be put in alow-power wake-on-motion mode or in another lower power mode thatconsumes less power than its high performance active mode. Theprocessing unit may also be placed into a low-power mode to conservepower. When motion or a certain motion pattern is detected, theaccelerometer and/or processing unit may switch into a higher power modeand additional sensors such as for example the gyroscope and/orproximity sensor may also be enabled. When a potential start of an eventis detected, memory variables for storing event-specific parameters,such as gesture types, gesture duration, etc. can be initialized.

In another example, upon detection of motion, the accelerometer switchesinto a higher power mode, but other sensors remain switched off untilthe data from the accelerometer indicates that the start of a behaviorevent has likely occurred. At that point in time, additional sensorssuch as the gyroscope and the proximity sensor may be enabled.

In another example, when there is no behavior event in progress, boththe accelerometer and gyroscope are enabled but at least one of eitherthe accelerometer or gyroscope is placed in a lower power mode comparedto their regular power mode. For example, the sampling rate may bereduced to conserve power. Similarly, the circuitry required to transferdata from electronic device 218 to electronic device 219 may be placedin a lower power mode. For example, radio circuitry 764 could bedisabled completely. Similarly, the circuitry required to transfer datafrom electronic device 218 to electronic device 219 may be placed in alower power mode. For example, it could be disabled completely until apossible or likely start of a behavior event has been determined.Alternatively, it may remain enabled but in a low power state tomaintain the connection between electronic device 218 and electronicdevice 219 but without transferring sensor data.

In yet another example, all motion-detection related circuitry,including the accelerometer may be switched off, if based on certainmeta-data it is determined that the occurrence of a particular behaviorevent such as a food intake event is unlikely. This may for example bedesirable to further conserve power. Meta-data used to make thisdetermination may, among other things, include one or more of thefollowing: time of the day, location, ambient light levels, proximitysensing, and detection that wearable device 770 has been removed fromthe wrist or hand, detection that wearable device 770 is being charged.Meta-data may be generated and collected inside wearable device 770.Alternatively, meta-data may be collected inside the mobile phone orinside another device that is external to wearable device 770 and to themobile phone and that can either directly or indirectly exchangeinformation with the mobile phone and/or wearable device 770. It is alsopossible that some of the meta-data are generated and collected insidewearable device 770 whereas other meta-data are generated and collectedin a device that is external to wearable device 770. In case some or allof the meta-data are generated and collected external to wearable device770, wearable device 770 may periodically or from time to time power upits radio circuitry 764 to retrieve meta-data related information fromthe mobile phone or other external device.

In yet another embodiment of the invention, some or all of the sensorsmay be turned on or placed in a higher power mode if certain meta-dataindicates that the occurrence of a particular behavior event, like forexample a food intake event is likely. Meta-data used to make thisdetermination may, among other things, include one or more of thefollowing: time of the day, location, ambient light levels and proximitysensing. Some or all of the meta-data may be collected inside the mobilephone or inside another device that is external to wearable device 770and to the mobile phone and that can either directly or indirectlyexchange information with the mobile phone and/or wearable device 770.In case some or all of the meta-data are generated and collectedexternal to wearable device 770, wearable device 770 may periodically orfrom time to time power up its radio circuitry 764 to retrieve meta-datarelated information from the mobile phone or other external device.

The detection of the start of a behavior event, such as for example afood intake event may be signaled to the user via one of the availableuser interfaces on wearable device 770 or on the mobile phone to whichwearable device 770 is connected. As one example, haptic interface 761inside wearable device 770 may be used for this purpose. Other signalingmethods are also possible.

The detection of the start of a behavior event such as for example afood intake event may trigger some or all of the sensors to be placed orremain in a high-power mode or active mode to track certain aspects of auser's eating behavior for a portion or for the entirety of the foodintake event. One or more sensors may be powered down or placed in alower power mode when or sometime after the actual or probable end ofthe behavior event (the deemed end of the behavior event) has beendetected. Alternatively, it is also possible that one or more sensorsare powered down or placed in a lower power mode after a fixed orprogrammable period of time.

Sensor data used to track certain aspects of a user's behavior, such asfor example a user's eating behavior, may be stored locally insidememory 766 of wearable device 770 and processed locally using processingunit 767 inside wearable device 770. Sensor data may also be transferredto the mobile phone or remote compute server, using radio circuitry 764,for further processing and analysis. It is also possible that some ofthe processing and analysis is done locally inside wearable device 770and other processing and analysis is done on the mobile phone or on aremote compute server.

The detection of the start of a behavior event, such as for example thestart of a food intake event, may trigger the power up and/or activationof additional sensors and circuitry such as for example the cameramodule 751. Power up and/or activation of additional sensors andcircuitry may happen at the same time as the detection of the start of afood intake event or sometime later. Specific sensors and circuitry maybe turned on only at specific times during a food intake event whenneeded and may be switched off otherwise to conserve power.

It is also possible that the camera module 751 only gets powered up oractivated upon explicit user intervention such as for example pushingand holding a button 759. Releasing the button 759 may turn off thecamera module 751 again to conserve power.

When the camera module 751 is powered up, projecting light source 752may also be enabled to provide visual feedback to the user about thearea that is within view of the camera. Alternatively, projecting lightsource 752 may only be activated sometime after the camera module hasbeen activated. In certain cases, additional conditions may need to bemet before projecting light source 752 gets activated. Such conditionsmay, among other things, include the determination that projecting lightsource 752 is likely aiming in the direction of the object of interest,or the determination that the wearable device is not moving excessively.

In one specific implementation, partially depressing button 759 onwearable device 770 may power up the camera module 751 and projectinglight source 752. Further depressing button 759 may trigger cameramodule 751 to take one or more still images or one or more streamingimages. In certain cases, further depressing button 759 may trigger ade-activation, a modified brightness, a modified color, or a modifiedpattern of projected light source 752 either before or coinciding withthe image capture. Release of button 759 may trigger a de-activationand/or power down of projected light source 752 and/or of camera module751.

Images may be tagged with additional information or meta-data such asfor example camera focal information, proximity information fromproximity sensor 756, ambient light levels information from ambientlight sensor 757, timing information etc. Such additional information ormeta-data may be used during the processing and analysis of food intakedata.

Various light patterns are possible and may be formed in various ways.For example, it may include a mirror or mechanism to reflect projectinglight source 752 such that projected light source 752 produces one ormore lines of light, outlines the center or boundaries a specific area,such as a cross, L-shape, circle, rectangle, multiple dots or linesframing the field of view or otherwise giving to the user visualfeedback about the field of view.

One or more Light Emitting Diodes (LEDs) may be used as projecting lightsource 752. The pattern of visible light may, among other things, beused by the user to adjust the position of the camera, adjust theposition the object of interest and/or remove any objects that areobstructing the line of sight between the object of interest and thecamera.

Projected light source 752 may also be used to communicate otherinformation to the user. As an example, the electronic device may useinputs from one or more proximity sensors, process those inputs todetermine if the camera is within the proper distance range from theobject of interest, and use one or more light sources to communicate tothe user that the camera is within the proper distance range, that theuser needs to increase the distance between camera and the object ofinterest, or that the user needs to reduce the distance between thecamera and the object of interest.

The light source may also be used in combination with an ambient lightsensor to communicate to the user if the ambient light is insufficientor too strong for an adequate quality image capture.

The light source may also be used to communicate information including,but not limited to, a low battery situation or a functional defect.

The light source may also be used to communicate dietary coachinginformation. As an example, the light source might, among other things,indicate if not enough or too much time has expired since the previousfood intake event, or may communicate to the user how he/she is doingagainst specific dietary goals.

Signaling mechanisms to convey specific messages using one or moreprojecting light sources may include, but are not limited to, one ormore of the following: specific light intensities or light intensitypatterns, specific light colors or light color patterns, specificspatial or temporal light patterns. Multiple mechanisms may also becombined to signal one specific message.

Microphone 758 may be used by the user to add specific or custom labelsor messages to a food intake event and/or image. Audio snippets may beprocessed by a voice recognition engine.

In certain embodiments, the accelerometer possibly combined with othersensors may, in addition to tracking at least one parameter that isdirectly related to food intake and/or eating behavior, also be used totrack one or more parameters that are not directly related to foodintake. Such parameters may, among other things, include activity, sleepor stress.

Specific Embodiments without Built-In Camera

In a different embodiment, electronic device 218 need not have anybuilt-in any image capture capabilities. Electronic device 218 may be awearable device such as a bracelet or wristband worn around the arm orwrist, or a ring worn around the finger. Electronic device 219 may be amobile phone and central processing and storage unit 220 may be one ormore compute servers and data storage that are located at a remotelocation.

In such embodiments, the food intake tracking and feedback system maynot use images to extract information about food intake and/or eatingbehavior. Alternatively, the food intake tracking and feedback systemmay leverage image capture capabilities that are available inside otherdevices, such as for example electronic device 219 or otherwise anelectronic device that is external to electronic device 218.

Upon detection or prediction of the start of a food intake event,electronic device 218 may send a signal to electronic device 219, or tothe electronic device that is otherwise housing the image capturecapabilities to indicate that the actual, probable or imminent start ofa food intake event has occurred. This may trigger electronic device219, or the electronic device that is otherwise housing the imagecapture capabilities to enter a mode that will allow the user to capturean image with at least one less user step compared to its default modeor standby mode.

As an example, if the image capture capabilities are housed withinelectronic device 219 and electronic device 219 is a mobile phone, atablet or a similar mobile device, electronic device 218 may send one ormore signals to software that has been installed on electronic device219 to indicate the actual, probable or imminent start of a food intakeevent. Upon receiving such signal or signals, the software on electronicdevice 219 may, among other things, take one or more of the followingactions: unlock the screen of electronic device 219, open the MobileApplication related to the food intake and feedback subsystem, activateelectronic device's 219 camera mode, push a notification to electronicdevice's 219 display to help a user with image capture, send a messageto electronic device 218 to alert, remind and/or help a user with imagecapture.

After image capture by electronic device 219, or the electronic devicethat is otherwise housing the image capture capabilities, electronicdevice 219, or the electronic device that is otherwise housing the imagecapture capabilities, may give visual feedback to the user. Examples ofvisual feedback may include a pattern, shape or overlay showingrecommended portion sizes, or a pattern, shape or overlay shade in oneor more colors and/or with one or more brightness levels to indicate howhealthy the food is. Other examples are also possible.

Integration with Insulin Therapy System

One or more components of the food intake tracking and feedback systempresented in this disclosure may interface to or be integrated with aninsulin therapy system. In one specific example, upon detection of thestart of a food intake event, feedback may be sent to the wearer toremind him or her to take a glucose level measurement and/or administerthe proper dosage of insulin. One or more additional reminders may besent over the course of the food intake event.

The food intake tracking and feedback system described in thisdisclosure, or components thereof may also be used by patients who havebeen diagnosed with Type I or Type II diabetes. For example, componentsdescribed in the current disclosure may be used to detect automaticallywhen a person starts, is about to start, or has started eating ordrinking. The detection of the start of a food intake event may be usedto send a message to the wearer at or near the start of a food intakeevent to remind him or her to take a glucose level measurement and/oradminister the proper dosage of insulin. The messaging may be automaticand stand alone. Alternatively, the system may be integrated with awellness system or a healthcare maintenance and reminder system. Thewellness system or the healthcare maintenance and reminder system mayupon getting notified that the start of a food intake event has beendetected send a message to the wearer. The wellness system or thehealthcare maintenance and reminder system may receive additionalinformation about the food intake event, such as the number of bites orsips, the estimated amount of food consumed, the duration of the meal,the pace of eating etc. The wellness system or the healthcaremaintenance and reminder system may send additional messages to thewearer during or after the food intake event based on the additionalinformation.

In another example, specific information about the content of the foodintake may be used as an input, possibly combined with one or more otherinputs, to compute the proper dosage of insulin to be administered.Information about food intake content may, among other things, includeone or more of the following: amount of carbohydrates, amounts ofsugars, amounts of fat, portion size, and molecular food category suchas solids or liquids. Real-time, near real-time as well as historicalinformation related food intake and eating patterns and behaviors may beincluded as inputs or parameters for computation of insulin dosages.

Other inputs that may be used as inputs or parameters to the algorithmsthat are used to compute insulin dosages may include, among otherthings, one or more of the following: age, gender, weight, historicaland real-time blood glucose levels, historical and real-time activity,sleep and stress levels, vital sign information or other informationindicative of the physical or emotional health of an individual.

Computation of insulin dosages may be done fully manually by the user,fully autonomously by a closed loop insulin therapy system orsemi-autonomously where some or all of the computation is done by aninsulin therapy system but some user intervention is still required.User intervention may, among other things, include activation of theinsulin therapy computation unit, confirmation of the dosage, interveneor suspend insulin delivery in case user detects or identifies ananomaly.

In one specific embodiment, the food intake tracking and feedback systemdescribed herein may upon detection of the actual, probable or imminentstart of a food intake event send one or more notifications to one ormore caregivers of the user, in addition or instead of sending anotification to the user.

The user may upon the start of a food intake event, optionally promptedby a notification or signal from the system or from a caregiver, takeone or more images of the food or meal to one or more caregiver. Thecaregiver may analyze the images and send information about the contentof the food back to the user. Information may, among other things,include estimation of certain macro-nutrient contents such as forexample carbohydrates, sugars or fats, estimation of caloric value,advice on portion size.

In case the user is on an insulin therapy, additional information suchas for example blood glucose level readings may also be sent to thecaregiver, and information provided by a caregiver back to a user mayalso include advice on the insulin dosage to be administered and thetiming when such insulin dosage or dosages should be administered. Incertain implementations, the caregiver may not be a person but anartificial intelligence system.

Gesture Recognition

In the various systems described herein, accurate determination ofgesture information can be important. For example, it would be useful todistinguish between a gesture connected with talking versus a gesturethat signals the start of an eating event period. Some gestures might beeasy to detect, such as the gesture of swinging an arm while walking,and thus measuring pace and number of steps, but other gestures might bemore difficult, such as determining when a user is taking a bite offood, taking a drink, biting their nails, etc. The latter can be usefulfor assessing precursor behaviors. For example, suppose a healthmaintenance and reminder system detects a pattern of nail bitinggestures followed five to ten minutes later with gestures associatedwith stress eating. The user might program their health maintenance andreminder system to signal them two minutes after nail biting so that theuser becomes aware and more in tune with their behavior that wouldotherwise go unnoticed. For this to work, gesture detection should beaccurate and reliable. This can be a problem where there is not a simplecorrelation between, say, movement of an accelerometer in a wearablebracelet and stress eating. Part of the reason for this is that thegestures that are of interest to the health maintenance and remindersystem are not easily derived from a simple sensor reading.

Being able to determine whether a user is taking a bite of food ortaking a sip of a drink, and being able to distinguish a bite from asip, can be useful to provide proper weight management guidance. Forexample, a weight management monitoring and reminder system may monitora user's food intake events from gestures. The weight managementmonitoring and reminder system may furthermore monitor a user's fluidintake events from gestures. Studies have shown that drinking sufficientwater at the start or close to the start of a meal and further drinkingsufficiently throughout the meal reduces food consumption and helps withweight loss. The user, the user's coach, the user's healthcare provider,or the provider of the weight management monitoring and reminder systemmay program the system such that it sends a reminder when a user startseating without drinking or if it detects that the user is not drinkingsufficiently throughout the meal. The system could also monitor theuser's fluid intake throughout the day and be programmed to sendreminders if the level of fluid intake does not meet the pre-configuredlevel for a particular time of day. For this to work, the gesturedetection should be reliable and accurate. This can be a problem whereit is necessary to distinguish between gestures that have lots ofsimilarities, such as for example distinguishing an eating gesture froma drinking gesture.

In various embodiments described herein, a processing system (comprisingprogram code, logic, hardware, and/or software, etc.) takes in sensordata generated by electronic devices or other elements based onactivities of a user. The sensor data might represent a snapshot of areading at a specific time or might represent readings over a span oftime. The sensors might be accelerometers, gyroscopes, magnetometers,thermometers, light meters and the like. From the sensor data, theprocessing system uses stored rules and internal data (such asinformation about what sensors are used and past history of use) toidentify behavior events wherein a behavior event is a sequence ofgestures and the gestures are determined from logical arrangement ofsensor data having a start time, sensor readings, and an end time, aswell as external data. The behavior event might be a high-level event,such as eating a meal, etc.

The determination of the boundaries of gestures, i.e., their start andend times, can be determined using methods described herein. Together,the data of a start time, sensor readings, and an end time is referredto herein as a gesture envelope. The gesture envelope might also includean anchor time, which is a data element defining a single time that isassociated with that gesture envelope. The anchor time might be themidpoint between the start time and the end time, but might be based onsome criteria based on the sensor data of the gesture envelope. Ananchor time might be outside of the time span from the start time to theend time. Multiple anchor times per gesture are also possible.

A machine classifier, as part of the processing system (but can also bea separate computer system, and possibly separated by a network of somekind), determines from a gesture envelope what class of gesture mighthave resulted in that gesture envelope's sensor data and details of thegesture. For example, the machine classifier might output that thesensor data indicates or suggests that a person wearing a bracelet thatincludes sensors was taking a walk, talking a bite to eat, or pointingat something.

With such a system, if gestures can be accurately discerned, then ahealth maintenance and reminder system (or other system that usesgesture information) can accurately respond to gestures made. In anexample described below, there is a set of sensors, or at least inputsfrom a set of sensors, coupled to a machine classification system thatoutputs gesture data from sensor readings, taking into account rules andstored data derived from training the machine classification system. Atraining subsystem might be used to train the machine classificationsystem and thereby forming the stored data derived from training. Eachof these components might use distinct hardware, or shared hardware, andcan be localized and/or remote. In general, when a gesture is detected,a system can analyze that gesture, determine likely actual, probable orimminent activities and provide the user feedback with respect to thoseactivities. For example, a vibration as a feedback signal to indicatethat the user has previously set up the system to alert the user whenthe user has been drinking for a semi-continuous period of more than 45minutes or that the user has reached their target for the amount ofwalking to be done in one session.

FIG. 8 is an illustrative example of a typical machine classificationsystem. The machine classification system of FIG. 8 includes a trainingsubsystem 801 and a detector subsystem 802. In some embodiments of thepresent disclosure, the machine classification system may includeadditional subsystems or modified versions of the subsystems shown inFIG. 8 . Training subsystem 801 uses training data inputs 803 and labels804 to train trained classifier model 805. Labels 804 may have beenassigned manually by a human or may have been generated automatically orsemi-automatically. Trained classifier model 805 is then used indetector subsystem 802 to generate classification output 806corresponding to a new unlabeled data input.

The stored sensor data includes temporal components. Raw sensor readingsare tagged with their time of reading. The raw sensor data can be drawnfrom accelerometers, gyroscopes, magnetometers, thermometers,barometers, humidity sensors, ECG sensors and the like, and temporaldata can come from other sources. Other examples of temporal sourcesmight be audio, voice or video recordings.

Illustrative examples of training subsystem 801 and detector subsystem802 in accordance with at least one embodiment of the present disclosureare shown in FIG. 9 and FIG. 10 respectively. Temporal training data 907and labels 912 are fed into classifier training subsystem of FIG. 8 .

As explained in the examples herein, raw sensor data is processed toidentify macro signature events. The macro signature events can delimitgestures that comprise sensor data over a period of time. The detectorsubsystem, or other system, can create a gesture envelope datasetcomprising a start time, an end time, one or more anchor times, metadataand sensor data that occurred within that gesture's time envelope fromthe start time to the end time.

For example, in the case of a gesture recognition problem, the gestureenvelope detector may identify specific time segments in the rawtemporal data that are indicative of a possible gesture. The gestureenvelope detector also generates a time envelope that specifies relevanttimes or segments of time within the gesture. Information included inthe time envelope may among other things include start time of thegesture, end time of the gesture, time or times within the gesture thatspecify relevant gesture sub-segments, time or times within the gesturethat specify relevant gesture anchor times (points) and possibly othermetadata, and raw sensor data from within the gesture's time envelope.

As an example of other metadata, suppose historical patterns suggestthat a wearer would have an eating session following a telephone callfrom a particular phone number. The electronic device can signal to thewearer about this condition, to provide conscious awareness of thepattern, which can help alter behavior if the wearer so decides.

Temporal training data 907 are fed into a gesture envelope detector 908.Gesture envelope detector 908 processes temporal training data 907 andidentifies possible instances of gestures 909 and a correspondinggesture time envelope from temporal training data 907. Temporal trainingdata 907 may comprise motion sensor data and gesture envelope detector908 may be processing the motion sensor data and identify gestures 909based on changes in pitch angle. In one embodiment, gesture envelopedetector 908 may detect the start of a gesture based on the detection ofa rise in pitch angle above a specified value and the end of an eventbased on the pitch angle dropping below a specified value. Other startand end criteria are also possible. An example of anchor points that maybe detected by gesture envelope detector 908 and specified by thegesture time envelope would be the time within the gesture segment whenthe pitch angle reaches a maximum. Other examples of anchor points arealso possible.

Gesture envelope detector 908 may add additional criteria to furtherqualify the segment as a valid gesture. For example, a threshold couldbe specified for the peak pitch angle or the average pitch angle withinthe segment. In another example, minimum and/or maximum limits may bespecified for the overall segment duration or for the duration ofsub-segments within the overall segment. Other criteria are alsopossible. Hysteresis may be employed to reduce the sensitivity to noisejitters.

In other embodiments of the present disclosure, gesture envelopedetector 908 may monitor other metrics derived from the input providingtemporal training data 907 and use those metrics to detect gestures.Examples of other metrics include but are not limited to roll angle,yaw, first or higher order derivative, or first or higher orderintegration of motion sensor data. Temporal data may be, or may include,data other than motion sensor data. In some embodiments of the presentdisclosure, a gesture envelope detector 908 may monitor and use multiplemetrics to detect gestures or to specify the gesture time envelope.

Gestures 909 along with gesture time envelope information, combined withtemporal training data 907 are fed into a feature generator module 910.Feature generator module 910 computes one or more gesture features usinginformation from temporal training data 907, the gesture time envelope,or a combination of information from temporal training data 907 and thegesture time envelope. In some embodiments of the present disclosure,feature generator module 910 computes one or more gesture features fromtemporal training data 907 within or over a time segment that fallswithin the gesture time envelope. It is also possible that featuregenerator module 910 computes one or more gesture features from temporaltraining data 907 within or over a time segment that does not fallwithin or that only partially falls within the gesture time envelope,but that is still related to the gesture time envelope. An example wouldbe a gesture feature that is computed from temporal training data 907over a time period immediately preceding the start of the gesture timeenvelope or over a time period immediately following the end of thegesture time envelope.

In some embodiments, feature generator module 910 may create one or morefeatures based on gesture time envelope information directly withoutusing temporal training data 907. Examples of such features may include,but are not limited to, total duration of the gesture time envelope,elapsed time since a last prior gesture, a time until next gesture, ordurations of specific sub-segments within the overall gesture timeenvelope or event time envelope.

In one embodiment, temporal training data 907 may be motion sensor dataand features may include read of pitch, roll and/or yaw angles computedwithin, or over, one or more sub-segments that are inside or around thegesture time envelope. Features may also include minimum, maximum, mean,variance, first or higher order derivative, first or higher orderintegrals of various motion sensor data inputs computed within or overone or more sub-segments that are inside or around the gesture timeenvelope. Features may also include distance traveled along a specificsensor axis or in a specific direction computed within or over one ormore sub-segments that are inside or around the gesture time envelope

Temporal training data 907 may be, or may include, data other thanmotion sensor data, such as sensor signals from one or more of thesensors described herein. Sub-segments within or over which featuregenerator module 910 computes features may be chosen based on timepoints or time segments specified by the gesture time envelope.Sub-segments may also be chosen based on time points or time segmentsfrom multiple gesture envelopes, such as for example adjacent gesturesor gestures that may not be adjacent but are otherwise in closeproximity.

Some embodiments may use a plurality of gesture envelope detectors, inparallel or otherwise. Parallel gesture envelope detectors may operateon a different subset of the sensor data, may use different thresholdsor criteria to qualify gestures, etc. For example, in case of gesturerecognition based on motion sensor data inputs, one gesture envelopedetector may use the pitch angle, whereas a second, parallel gestureenvelope detector may use roll angle. One of the gesture envelopedetectors may be the primary gesture envelope detector, whereas one ormore additional gesture envelope detectors may serve as secondarygesture envelope detectors. The Feature Generation logic may processgestures generated by the primary gesture envelope detector, but maygleam features derived using information from gesture time envelopes ofnearby gestures (in time) obtained from one or more secondary, parallelenvelope detectors.

Training data might comprise a plurality of gesture envelope datasets,each having an associated label representing a gesture (such as aselection from a list of gesture labels), provided manually, in a testenvironment, or in some other manner. This training data, with theassociated labels, can be used to train the machine classifier, so thatit can later process a gesture envelope of an unknown gesture anddetermine the gesture label most appropriately matching that gestureenvelope. Depending on the classification method used, the training setmay either be cleaned, but otherwise raw data (unsupervisedclassification) or a set of features derived from cleaned, but otherwiseraw data (supervised classification).

Regardless of the classification method, defining the proper databoundaries for each label is important to the performance of theclassifier. Defining the proper data boundaries can be a challenge incase of temporal problems, i.e., problems whereby at least one of thedata inputs has a time dimension associated with it. This isparticularly true if the time dimension is variable or dynamic and iffeatures that are linked to specific segments of the variable timeenvelope or to the overall variable time envelope contribute materiallyto the performance of the classifier.

One example of such a temporal problem is gesture recognition, such asfor example the detection of an eating or drinking gesture from rawmotion sensor data. The duration of a bite or sip may varyperson-to-person and may depend on the meal scenario or specifics of thefoods being consumed.

The output of the feature generator module 910 is a set of gestures 911with corresponding time envelope and features. Before gestures 911 canbe fed into Classifier Training module 915, labels 912 from the trainingdataset need to be mapped to their corresponding gesture. This mappingoperation is performed by the Label Mapper module 913.

In some embodiments, the timestamps associated with labels 912 alwaysfall within the time envelope of their corresponding gesture. In thatcase, the logic of Label Mapper module 913 can be a look up where thetimestamp of each label is compared to the start and end time of eachgesture time envelope and each label is mapped to the gesture for whichthe timestamp of the label is larger than the start time of therespective gesture time envelope and smaller than the end time of therespective gesture time envelope. Gestures for which there is nocorresponding label may be labeled as “NEGATIVE”, indicating it does notcorrespond to any labels of interest.

However, in other embodiments of the present disclosure, the timestampof labels 912 may not always fall within a gesture time envelope. Thismay be due to the specifics of the procedures that were followed duringthe labeling process, timing uncertainty associated with the labelingprocess, unpredictability or variability in the actual raw data input,or an artifact of the gesture envelope detector logic. In such cases,the label mapper might be modified to adjust the boundaries of thegesture envelopes.

Gestures 914, characterized by features and a label, may then be fedinto Classifier Training module 915 to produce a trained statisticalmodel that can be used by the Detector subsystem. Classifier Trainingmodule 915 may use a statistical model such as a decision tree model, aK-nearest neighbors model, a Support Vector Machine model, a neuralnetworks model, a logistic regression model or other model appropriatefor a machine classification. In other variations, the structures of thetables and the data formats of the data used, as in FIG. 9 , may varyand be different from that shown in FIG. 9 .

FIG. 10 shows an illustrative example of a detector subsystem 802. Asshown there, unlabeled temporal data 1017 is fed into the detectorsubsystem of FIG. 10 . The detector subsystem includes gesture envelopedetector logic 1018 and feature generator logic 1020. Functionally,gesture envelope detector logic 1018 used by the detector subsystem issimilar to gesture envelope detector logic used by its correspondingtraining subsystem. Likewise, feature generator logic 1020 of thedetector subsystem is functionally similar to feature generator module910 of its corresponding training subsystem. In some embodiments,gesture envelope detector logic 1018 may monitor and use multiplemetrics to detect gestures or to specify the gesture time envelope.

However, the implementation of gesture envelope detector logic 1018 andfeature generator logic 1020 may be different in the training subsystemand its corresponding detector subsystem. For example, the detectorsubsystem may be implemented on hardware that is more power-constrained,in which case gesture envelope detector logic 1018 may need to beoptimized for lower power operation compared to its counterpart used inthe corresponding training subsystem. The detector subsystem may alsohave more stringent latency requirements compared to the trainingsystem. If this is the case, gesture envelope detector logic 1018 usedin the detector subsystem may need to be designed and implemented forlower latency compared to its counterpart used in the correspondingtraining subsystem.

An output of feature generator logic 1020 is fed into detector logic1022, which classifies the gesture based on the trained classifiermodule from its corresponding training subsystem. The ClassificationOutput may include one or more labels. Optionally, detector 1022 mayalso assign a confidence level to each label.

Classification on Combination of Temporal and Non-Temporal Data Inputs

In another embodiment, inputs into the classification system may includea combination of temporal and non-temporal data. FIG. 11 is anillustrative example of a training subsystem in accordance with at leastone embodiment of the present disclosure where at least some of the datainputs are temporal and at least some of the data inputs arenon-temporal. Other implementations are also possible.

Non-temporal training data 1129 do not need to be processed by gestureenvelope detector 1125 and feature generator Logic 1127. Non-temporaltraining data 1129 may be fed directly into the label mapper logic 1132along with labels 1131. In some embodiments, non-temporal training datamay be processed by a separate feature generator module, non-temporalfeature generator module 1130, to extract specific non-temporal featuresof interest, which are then fed into Label mapper logic 1132. Labelmapper logic 1132 may assign the labels 1131, along with non-temporalfeatures 1136 that are attached to the label to gestures using methodssimilar to the methods for mapping labels to gestures that have beendescribed herein.

FIG. 12 is an illustrative example of a classification detectorsubsystem in accordance with at least one embodiment of the presentdisclosure where at least some of the data inputs are temporal and atleast some of the data inputs are non-temporal.

Unsupervised Classification of Temporal Data Inputs

In yet another embodiment of the present disclosure, deep learningalgorithms may be used for machine classification. Classification usingdeep learning algorithms is sometimes referred to as unsupervisedclassification. With unsupervised classification, the statistical deeplearning algorithms perform the classification task based on processingof the data directly, thereby eliminating the need for a featuregeneration step.

FIG. 13 shows an illustrative example of a classifier training subsystemin accordance with at least one embodiment of the present disclosurewhere the classifier training module is based on statistical deeplearning algorithms for unsupervised classification.

Gesture envelope detector 1349 computes gestures 1350 with correspondinggesture time envelopes from temporal training data 1348. Data segmentor1351 assigns the proper data segment or data segments to each gesturebased on information in the gesture time envelope. As an example, datasegmentor 1351 may look at the start and end time information in thegesture time envelope and assign one or more data segments thatcorrespond to the overall gesture duration. This is just one example.Data segments may be selected based on different segments orsub-segments defined by the gesture time envelope. Data segments couldalso be selected based on time segments that are outside of the gesturetime envelope but directly or indirectly related to the gesture timeenvelope. An example could be selection of data segments correspondingto a period of time immediately preceding the start of the gesture timeenvelope or selection of data segments corresponding to a period of timeimmediately following the end of the gesture time envelope. Otherexamples of time segments that are outside the gesture time envelope butdirectly or indirectly related to the gesture time envelope are alsopossible.

Gestures including data segments, gesture time envelope information andlabels are fed into classifier training module 1356. In some embodimentsof the present disclosure, only a subset of the gesture time envelopeinformation may be fed into classifier training module 1356. In someembodiments of the present disclosure, gesture time envelope informationmay be processed before it is being applied to classifier trainingmodule 1356. One example could be to make the time reference of thegesture time envelope align with the start of the data segment, ratherthan with the time base of the original temporal training data stream.Other examples are also possible. By adding time envelope informationthat further characterizes the data segments, the performance of theclassifier training module may be improved.

For example, in case of gesture recognition of eating gestures based onmotion sensor data inputs, feeding additional anchor time informationsuch as the time when the pitch angle, roll or yaw reaches a maximum orminimum into the classifier training module can improve the performanceof a trained classifier 1357 as trained classifier 1357 can analyze thetraining data and look for features and correlations specifically aroundsaid anchor times. Other examples of time envelope information that canbe fed into the classifier training module are also possible.

FIG. 14 shows an illustrative example of a classification detectorsubsystem in accordance with at least one embodiment of the presentdisclosure that could be used in combination with classificationtraining subsystem of FIG. 13 .

Classifier Ensemble

In some embodiments, multiple parallel classification systems based ongesture envelope detection may be used. An example of a system withmultiple parallel classifiers is shown in FIG. 15 . The number ofparallel classification systems may vary. Each classification system1510, 1512, 1514 has its own training and detector sub-system andperforms gesture envelope detection on a different subset of thetraining data 1502 and labels 1504 inputs to detect gestures, or may usedifferent thresholds or criteria to qualify gestures. Consequently, eachindividual gesture envelope detector will generate an independent set ofgestures each with different gesture time envelopes. The featuregenerator logic of each classification system creates features for thegestures created by its corresponding gesture envelope detector logic.The features may be different for each classification system. Theclassifier model used by each of the parallel classifiers may be thesame or different, or some may be the same and others may be different.Since the gesture time envelopes and features used for training of eachclassifier model are different, the parallel classification systems willproduce different Classification Outputs 1516, 1518, 1520.

The Classification Outputs 1516, 1518, 1520 of each classificationsystem may be fed into Classifier Combiner sub-system 1522. ClassifierCombiner sub-system 1522 may combine and weigh the ClassificationOutputs 1516, 1518, 1520 of the individual classification systems 1510,1512, 1514 to produce a single, overall Classification result, CombinedClassification Output 1524. The weighing may be static or dynamic. Forexample, in case of gesture recognition, certain classifiers may performbetter at correctly predicting the gestures of one group of people,whereas other classifiers may perform better at correctly predicting thegestures of another group of people. Classifier Combiner sub-system 1522may use different weights for different users or for differentcontextual conditions to improve the performance of the overallclassifier ensemble. The trained system can then be used to processunlabeled data 1506.

Other examples of temporal problems include but are not limited toautonomous driving, driver warning systems (that alert the driver whendangerous traffic conditions are detected), driver alertness detection,speech recognition, video classification (security camera monitoring,etc.) and weather pattern identification.

Ignoring the temporal nature of the data inputs as well as any featuresthat are linked to the temporal envelope of the data inputs can limitperformance of the classifier and make the classifier non-suitable forclassification tasks where a reliable detection depends on features thatare inherently linked to segments of the variable time envelope or tothe overall variable time envelope. Performance and usability can breakdown if a proper time period cannot be determined reliably, or where thetime period varies from gesture-to-gesture, from person-to-person etc.

As described herein, improved methods frame temporal problems with avariable time envelope, so that information tied to the overall variabletime envelope or to segments thereof can be extracted and included inthe feature set used to train the classifier. The proposed improvedmethods improve performance and reduce the amount of training dataneeded since features can be defined relative to the time bounds of thevariable time envelope, thereby reducing sensitivities to time and uservariances.

In addition to finding time envelopes for gestures, the system can alsofind event time envelopes. In such an approach, the system mightdetermine a gesture and a gesture envelope, but then do so foradditional gestures and then define an event envelope, such as the startand end of an eating event.

Context to Improve Overall Accuracy

FIG. 16 shows an example of a machine classification system thatincludes a cross-correlated analytics sub-system. Classification output1602 may be fed into cross-correlated analytics sub-system 1604.Cross-correlated analytics sub-system 1604 can make adjustments basedone or more contextual clues to improve the accuracy. In the example ofgesture recognition, an example of a contextual clue could be theproximity in time to other predicted gestures. For example, eatinggestures tend to be grouped together in time as part of an eatingactivity such as a meal or a snack. As one example, Cross-correlatedanalytics sub-system 1604 could increase the confidence level that apredicted gesture is an eating gesture based on the confidence level anddegree of proximity of nearby predictions.

In another embodiment, cross-correlated analytics sub-system 1604 maytake individual predicted gestures 1614 from classification output 1602as inputs and may cluster individual predicted gestures into predictedactivities 1608. For example, cross-correlated analytics sub-system 1604may map multiple bite gestures to an eating activity such as a snack ora meal. Likewise, cross-correlated analytics sub-system 1604 could mapmultiple sip gestures to a drinking activity. Other examples of activityprediction based on gesture clustering are also possible.Cross-correlated analytics sub-system 1604 may modify the confidencelevel of a predicted gesture based on the temporal spacing and sequenceof predicted activities. As an example, Cross-correlated analyticssub-system 1604 could decrease the confidence level that a predictedgesture is an eating gesture if it is detected shortly following or amida “brushing teeth” activity. In another example, Cross-correlatedanalytics sub-system 1604 could decrease the confidence level that apredicted gesture is a drinking gesture if it is detected during orshortly after a brushing teeth activity. In this case, Cross-correlatedanalytics sub-system 1604 could decide to increase the confidence levelthat the gesture is a rinsing gesture.

Cross-correlated analytics sub-system 1604 can adjust a classificationoutput of a predicted gesture based on historical information 1612 orother non-gesture meta-data 1610 information such as location, date andtime, other biometric inputs, calendar or phone call activityinformation. For example, Cross-correlated analytics sub-system 1604 mayincrease the confidence level that a predicted gesture is an eatinggesture or a predicted activity is an eating activity if GPS coordinatesindicate that the person is at a restaurant. In another example,Cross-correlated analytics sub-system 1604 may increase the confidencelevel that a predicted gesture is an eating gesture or a predictedactivity is an eating activity if it occurs at a time of day for whichpast behavior indicates that the user typically engages in eating atthis time of the day. In yet another example of the present disclosure,cross-correlated analytics sub-system 1604 may increase the confidencelevel that a predicted gesture is an eating gesture or that a predictedactivity is an eating activity if the predicted gesture or predictedactivity is preceding or following a calendar event or phone callconversation if past behavior indicates that the user typically eatspreceding or following similar calendar events (e.g., with sameattendee(s), at certain location, with certain meeting agenda, etc.) orphone call conversation (e.g., from specific phone number). While theabove examples reference eating, it will be apparent to one skilled inthe art that this could also be applied to gestures other than eating.In the general case, the machine classifier with cross-correlatedanalytics sub-system uses contextual clues, historical information andinsights from proximity sensing in time to improve accuracy, where thespecific contextual clues, historical information and insights fromproximity sensing in time and how they are applied is determined bymethods disclosed or suggested herein.

In some embodiments of the present disclosure, Classification Output1602 may include additional features or gesture time envelopeinformation. Cross-correlated analytics sub-system 1604 may process suchadditional features or gesture time envelope information to determine orextract additional characteristics of the gesture or activity. As anexample, in one embodiment of the present disclosure, Cross-correlatedanalytics sub-system 1604 derives the estimated duration of the drinkinggesture from the gesture time envelope and this information can be usedby cross-correlated analytics sub-system 1604 or by one or more systemsthat are external to the machine classifier system to estimate the fluidintake associated with the drinking gesture.

In another embodiment, Cross-correlated analytics sub-system 1604 mayderive the estimated duration of an eating gesture from the gesture timeenvelope and this information may be used by the cross-correlatedanalytics sub-system 1604 or by one or more systems that are external tothe machine classifier system to estimate the size of the biteassociated with the eating gesture. Cross-correlated analyticssub-system 1604 may combine the predicted drinking gestures with othersensor data to predict more accurately if someone is consuming a drinkthat contains alcohol and estimate the amount of alcohol consumed.Examples of other sensor data may include but are not limited tomeasuring hand vibration, heart rate, voice analysis, skin temperature,measuring blood, breath chemistry or body chemistry.

Detector sub-system 1600 may predict a specific eating or drinkingmethod and cross-correlated analytics sub-system 1604 may combine theinformation obtained from detector sub-system 1600 about specifics ofthe eating or drinking method with additional meta-data to estimate thecontent, the healthiness or the caloric intake of the food. Examples ofeating/drinking methods may include but are not limited to eating withfork, eating with knife, eating with spoon, eating with fingers,drinking from glass, drinking from cup, drinking from straw, etc.).Examples of meta-data may include but are not limited to time of day,location, environmental or social factors.

Another Example Embodiment

FIG. 17 shows a high level functional diagram of a monitoring system ofa variation similar to that of FIG. 1 , in accordance with anembodiment. As shown in FIG. 17 , sensor units 1700 interact with anevent detection subsystem 1701 that in turn interacts with an objectinformation retrieval subsystem 1702, and that provide inputs to aprocessing and analysis system, the results of which can be stored indata storage units 1704.

In some embodiments, elements shown in FIG. 17 are implemented inelectronic hardware, while in others some elements are implemented insoftware and executed by a processor. Some functions might sharehardware and processor/memory resources and some functions might bedistributed. Functionality might be fully implemented in a sensor devicesuch as wrist worn wearable device, or functionality might beimplemented across the sensor device, a processing system that thesensor device communicates with, such as a smartphone, and/or a serversystem that handles some functionality remote from the sensor device.For example, a wearable sensor device might make measurements andcommunicate them to a mobile device, which may process the data receivedfrom the wearable sensor device and use that information possiblycombined with other data inputs, to activate object informationretrieval subsystem 1702. Object information retrieval subsystem 1702may be implemented on the mobile device, on the wearable sensor device,or on another electronic device. The object information retrievalsubsystem 1702 may also be distributed across multiple devices such asfor example across the mobile device and the wearable sensor device.Data or other information may be stored in a suitable format,distributed over multiple locations or centrally stored, in the formrecorded, or after some level of processing. Data may be storedtemporarily or permanently. Data may be stored locally on the wearablesensor device, on the mobile device or may be uploaded over the Internetto a server.

A first component of the system illustrated in FIG. 17 is the eventdetection subsystem 1701. One role of event detection subsystem 1701 isto identify the actual, probable or imminent occurrence of an event. Anevent could, for example, be an event related to a specific activity orbehavior. Specific activities or behaviors may include, but are notlimited to, eating, drinking, smoking, taking medication, brushingteeth, flossing, hand washing, putting on lipstick or mascara, shaving,making coffee, cooking, urinating, using the bathroom, driving,exercising or engaging in a specific sports activity. Other examples ofan event that may be detected by event detection subsystem 1701 could bean operator on a production line or elsewhere performing a specific taskor executing a specific procedure. Yet another example could be a robotor robotic arm performing a specific task or executing a specificprocedure on a production arm or elsewhere.

The event detection subsystem may use inputs from one or more sensorunits 1700, other user inputs 1705, or a combination of one or moresensor inputs from sensor unit 1700 and one or more other user inputs1705 to determine or infer the actual, probable or imminent occurrenceof an event. The event detection subsystem 1701 may do additionalprocessing on the sensor and/or user inputs to determine the actual,probable or imminent occurrence of an event. In general, the eventdetection system records and/or reacts to an inferred event, whichoccurs when the event detection system has inputs and/or data from whichit determines that an event might actually be starting, might likelystart, or is imminent. In some cases, the event detection system mightinfer an event where an event did not actually occur and process it asan event, but this might be an infrequent occurrence.

The event detection subsystem 1701 may also do additional processing onthe sensor and/or user inputs to determine additional information aboutthe event. Such information may include, but is not limited to, theduration of the event, the event start time, the event end time, metricsassociated with the speed or pace at which subject is engaging in theevent. Other event data elements are also possible. For example, if theevent is an eating event, an event data element could be the number ofbites or the amount of food consumed. Similarly, if the event is adrinking event, an event data element could be the number of sips or theamount of fluid intake. These event data elements might be stored in adatabase that maintains data elements about inferred events.

Using the gesture sensing technology, an event detection system cantrigger and external device to gather further information.

In a specific embodiment, the electronic device that houses objectinformation retrieval subsystem 1702 or a portion of object informationretrieval subsystem 1702 includes Near-Field-Communication (NFC)technology and the object information retrieval subsystem 1702 obtainsinformation about the object(s) or subject(s) with which the subject maybe interacting at least in part via transmission over a wireless NFClink.

In a specific embodiment, the external device is a NFC reader andvarious objects having NFC tags thereon are detected. The NFC readermight be integrated with the gesture sensing technology or with somecomponents of the gesture sensing technology.

Where those objects are food/beverage related, the event detectionsystem can determine what the gestures are related to. For example,food/beverage containers might have NFC tags embedded in the productpackaging and a food intake monitoring system might automaticallydetermine that gestures are related to an eating event, then signal toan NFC reader to turn on and read nearby NFC tags, thereby reading theNFC tags on the products being consumed so that the gestures and theevent are associated with a specific product.

In an example, a monitoring system might have a wearable device thatdetermines gestures and based on those gestures, identifies an eatingevent or a drinking event. Suppose a drinking event is detected andbased on the sensor inputs and gestures detected, the monitoring systemdetermined that the user drank three quarters of a can of soda, such asbe counting sips and estimating or computing the size of each sip. Asthe gestures are likely identical regardless of whether it is a dietsoda or a regular soda. Using the NFC reader, the specific brand andtype of soda can be detected as well.

The sensors may be in the wearable device, with gesture determinationlogic or processing occurring in an external device that iscommunicatively coupled to the wearable device, such as a mobile phone,or the gesture determination may partially happen on the wearable deviceand partially on the external device that is communicatively coupled tothe wearable device.

The NFC reader may be in the wearable device that houses the sensors, inan external device that is communicatively coupled to the wearabledevice and that performs at least some of the gesture determination orin another external device that is communicatively coupled to thewearable device, to an external device that performs at least some ofthe gesture determination or to both.

In the general case, the detection of the occurrence of an event may beused to initiate a process/system/circuitry/device to collectinformation about objects/items or other subjects that are beinginteracted with by the person performing the activity or behaviorrepresented by the event. This information may be recorded in the formof data elements. Object data elements may be stored in a database. Oneor more object data elements and one or more event data elements of thesame event may be recorded as a single entry in a database. Object dataelements and event data elements may also be recorded as separateentries in a database. Other data structures consistent with theteachings herein might also be used, or used instead.

When the NFC reader system gets activated, it initiates one or more NFCread commands, and additional information from relevant objects isobtained wirelessly over NFC link. Additional processing may be applied,such as filtering out the NFC data to simplify processing downstream.

In other variations, other wireless links are used instead of NFC orwith NFC. Examples of other wireless technologies include but are notlimited to Bluetooth, Bluetooth Low Energy, Wi-Fi, Wi-Fi derivatives,and proprietary wireless technologies.

The detection of occurrence of an event signal may be used to filter outinformation about specific, relevant objects that are related to theactivity/behavior of interest that are part of the event.

While the object detection process might be automatic, it can also haveuser intervention required to activate the object information retrievalsystem. For example, it could be as simple as asking the user to turn ona camera, or make a decision whether to continue the detection processand get the user's input on the decision. As another example, the usermay be prompted to move the NFC reader closer to the NFC tag ofinterest. In another example, the user may be prompted to activate theNFC reader circuitry, or portions of the NFC reader circuitry or takeone or more actions that allow the NFC reader circuitry to issue readcommand.

In addition to an NFC reader, a camera might be activated and additionalinformation from relevant objects is obtainable by analyzing images orvideo recording of relevant objects. The camera might be combined in aunit with the NFC reader. In some embodiments, only a camera is used, orother ancillary sensors are used to obtain additional information aboutthe event.

Information about the activity or behavior that are part of the event,obtained from the event detection subsystem can be combined withinformation about the object(s) or subject(s) the user is interactingwith, and additional processing/analysis may be performed on thecombined dataset to obtain additional information or insights about theactivity/behavior that cannot be obtained from looking at only one ofthe data sources alone.

While many examples in this disclosure refer to the event detectionsystem analyzing gestures to detect the actual, likely or imminentoccurrence of an event, other sensor inputs are also possible. Forexample, audio signals in or near the mouth, throat or chest may be usedto detect and obtain information about a user's consumption. Toaccomplish this, the event detection subsystem 1701 may use inputs 1706from one or more sensor units 1700. Sensors may include, but are notlimited to, accelerometers, gyroscopes, magnetometers, magnetic angularrate and gravity (MARG) sensors, image sensors, cameras, opticalsensors, proximity sensors, pressure sensors, odor sensors, gas sensors,glucose sensors, heart rate sensors, ECG sensors, thermometers, lightmeters, Global Positioning Systems (GPS), and microphones.

Upon the detection of an actual, probable or imminent occurrence of anevent, the event detection subsystem may activate the object informationretrieval subsystem 1702. Alternatively, the event detection subsystem1701 may do additional processing to decide whether or not to activatethe object information retrieval subsystem 1702. The event detectionsubsystem 1701 may activate the object information retrieval subsystem1702 immediately or wait for a certain time before activating the objectinformation retrieval subsystem 1702.

The role of object information retrieval subsystem 1702 is to collectinformation about objects or other subjects a subject is interactingwith when or some time after the event detection subsystem 1701 hasdetected the occurrence of an actual, probable or imminent event.

In one such embodiment, upon receiving an activation input signal 1707from event detection subsystem 1701, or some time after receiving anactivation input signal 1707, object information retrieval subsystem1702 initiates an NFC read action to read the NFC tag attached to,housed within or otherwise associated with one or more objects that arewithin NFC range of the device that houses object information retrievalsubsystem 1702. Object information retrieval subsystem 1702 sends thedata received from one or more objects to processing and analysissubsystem 1703. Object information retrieval subsystem 1702 may doadditional processing on the data before sending it to processing andanalysis subsystem 1703. In other embodiments, additional ancillarysensors or sensor systems might be used, such as a camera or otherelectronic device.

Processing may include but is not limited to filtering, extractingspecific data elements, modifying data or data elements, combining dataor data elements obtained from multiple objects, combing data or dataelements with data obtained from other sources not collected via NFC.Examples of filtering may include but are not limited to filtering basedon distance or estimated distance between the object or objects andobject information retrieval subsystem, based on signal strength ofreceived NFC signals, based on order in which data is received fromobjects, based on information in the data or in specific data elements.Other filtering mechanisms or criteria used for filtering are alsopossible. Object information retrieval subsystem 1702 may stop readingNFC tags after a fixed time, after a configurable time, after reading afixed or configurable number of tags. Other criteria may also be used.

It is also possible, that the object information retrieval subsystemcollects information about objects or other subjects a subject isinteracting with independent of the event detection subsystem. In suchan embodiment, the processing and analysis subsystem 1703 may usesignals 1708 received from event detection subsystem 1701 with datasignals 1709 received from object information retrieval subsystem 1702to deduce relevant information about objects or other subjects a subjectmay be interacting during an activity or when exhibiting a certainbehavior.

In a specific embodiment of this disclosure, object informationretrieval subsystem 1702 continuously, periodically or otherwiseindependently from inputs from event detection subsystem 1701 reads NFCtags from object that are within range of the electronic device thathouses object information retrieval subsystem 1702 in its entirety or inpart. The detection of occurrence of an activity/behavior signal may beused to filter out information about specific, relevant objects that arerelated to the activity/behavior of interest. Where the objects areassociated with an event, indications of, or references to, the specificobjects might be recorded as part of a record of the event, so that theinformation can be later used for filtering or other processing.

In a specific embodiment, object information retrieval subsystem 1702collects data independent from inputs it receives from the eventdetection subsystem but only sends data to processing and analysissubsystem 1703 when or some time after, it receives an activation inputsignal 1707 from event detection subsystem 1701. Object informationretrieval subsystem 1702 may send only a subset of the data it hasreceived from objects over an NFC link. For example, it may only senddata that it receives in a fixed or configurable time window related tothe time it receives the activation input signal 1707. For example, itmay only send data immediately preceding and/or immediately followingthe activation input signal 1707. Other time windows are also possible.Object information retrieval subsystem 1702 may do additional processingon the data before sending it to processing and analysis subsystem 1703.

In one embodiment of this disclosure, the object information retrievalsubsystem 1702 includes a camera and the information about the object orobjects may be derived from the analysis of an image or video recording.

In a preferred embodiment of this disclosure, object informationretrieval subsystem 1702 collects data from objects without anyintervention or input from the subject. In another embodiment of thisdisclosure, some user input or intervention is required. For example,the user may be prompted to move the NFC reader closer to the NFC tag ofinterest. In another example, the user may be prompted to activate theNFC reader circuitry, or portions of the NFC reader circuitry or takeone or more actions that allow the NFC reader circuitry to issue readcommand.

FIG. 18 shows a high level functional diagram of a monitoring system inaccordance with an embodiment that requires user intervention. As shownin FIG. 18 , one or more sensor units 1800 interact with event detectionsubsystem 1801 and send sensor data to event detection subsystem 1801.Upon detection or inference of an actual, probable or imminent event,event detection subsystem sends one or more notifications to userinteraction unit 1802 to request activation of NFC scan action.Notifications may be rendered to the user as a displayed text message,as a displayed image, as an audio message, as a LED signal, as avibration, etc. A combination of user interfaces may also be used. Otheruser interfaces may also be used. The user may respond to thenotification(s) using one or more user interfaces of the userinteraction unit 1802. A user response may trigger user interaction unit1802 to send a scan command 1810 to object information retrievalsubsystem 1803. Upon receiving scan command 1810 or some time afterreceiving scan command 1810, object information retrieval subsystem 1803may activate NFC reader 1806 and obtain information about object 1804over a wireless communication link 1811 between NFC reader 1806 and NFCtag 1807. Information obtained over wireless link 1811 may containinformation about brand, type, content, expiration date, lot number,etc. of object 1804. Other information about the object may also beretrieved. Event data elements 1814 from event detection subsystem 1801and object data elements 1813 retrieved by object information retrievalsubsystem 1803 from one or more objects over a wireless link 1811 may besent to processing and analysis subsystem 1805. Additional processingmay be performed and the event and object data elements may be stored ina database on one or more data storage units 1815.

In another example, the event detection system automatically scans forNFC tags, but upon receiving data from an object, the object informationretrieval subsystem sends a message to the subject and the subjectauthorizes, or not, transmission to a processor or analysis subsystem orthe subject confirms the information directly. This message may also besent by processing and analysis subsystem.

Processing by processing and analysis subsystem 1805 may include but isnot limited to filtering, extracting specific data elements, modifyingdata or data elements, combining data or data elements obtained frommultiple objects, combing data or data elements with data obtained fromother sources not collected via NFC. Examples of filtering may includebut are not limited to filtering based on distance or estimated distancebetween object or objects and object information retrieval subsystem,based on signal strength of received NFC signals, based on order inwhich data is received from objects, based on information in the data orin specific data elements. Other filtering mechanisms or criteria usedfor filtering are also possible. Object information retrieval subsystem1803 may stop sending data after a fixed time, after a configurabletime, after reading a fixed or configurable number of tags. Othercriteria may also be used. Object information retrieval subsystem 1803may send data only from a single object, from a subset of objects orfrom all objects for which it receives data within the specified timewindow.

In a different embodiment of this disclosure, object informationretrieval subsystem 1803 continuously, periodically or otherwiseindependently from inputs from event detection subsystem 1801 reads NFCtags from object that are within range of the electronic device thathouses object information retrieval subsystem 1803 in its entirety or inpart and sends such data to processing and analysis subsystem 1805independent of any signals from event detection subsystem.

Processing and analysis subsystem 1805 receives data inputs from eventdetection subsystem 1801 and object information retrieval subsystem1803. It is also possible that processing and analysis subsystem 1805receives inputs only from object information retrieval subsystem 1803.Processing and analysis subsystem 1805 may do additional processing onthe data and analyze the data to extract information about the object(s)or subject(s) from the data it receives. Processing may include but isnot limited to filtering, extracting specific data elements, modifyingdata or data elements, combining data or data elements obtained frommultiple objects. Analysis may also include comparing specific dataelements to data that is stored in a look up table or database,correlating data elements to data elements that were obtained at anearlier time and/or from different subjects. Other processing andanalysis steps are also possible. Processing and analysis subsystem 1805may store raw or processed data in data storage unit(s) 1804. Storagemay be temporary or permanent.

In some embodiments, the outputs from processing and analysis subsystem1805 may be available in real-time, either during or shortly after theevent. In other embodiments, the outputs may not be available until alater time.

Processing and analysis subsystem 1805 may be implemented on the mobiledevice, on the wearable sensor device, or on another electronic device.Processing and analysis subsystem 1805 may also be distributed acrossmultiple devices such as for example across the mobile device and thewearable sensor device. In another example, processing and analysissubsystem 1805 may be distributed across the mobile device and a localor remote server. Processing and analysis subsystem 1805 may also all beimplemented on a local or remote server. Information may be stored in asuitable format, distributed over multiple locations or centrallystored, in the form recorded, or after some level of processing. Datamay be stored temporarily or permanently. Data may be stored locally onthe wearable sensor device, on the mobile device or may be uploaded overthe Internet to a server.

The object information retrieval subsystem may be housed in its entiretyor in part inside a battery operated electronic device and it may bedesirable to minimize the power consumption of the object informationretrieval subsystem. When no event is detected, the radio circuitry(e.g., the NFC reader circuitry) may be placed in a low power state.Upon detection or inference of an actual, probable or imminentoccurrence of an event, the object information retrieval subsystem maybe placed in a higher power state. One or more additional circuitryinside the object information retrieval subsystem may be powered up toactivate the object information retrieval subsystem, to improve therange or performance of object information retrieval subsystem, etc. Inone specific example, the NFC reader is disabled or placed in a lowpower standby or sleep mode when no event is detected. Upon detection orinference of an event, the NFC reader is placed in a higher power statein which it can communicate with NFC tags of neighboring objects. Afterreading a pre-configured number of NFC tags, after a pre-configured timeor upon detection of the end or completion of the event, the NFC readermay be disabled again or may be placed back into a low power standby orsleep mode.

Medication Dispensing System Examples

A system according to the principles and details described herein mightbe used to detect onset of eating/drinking to start administering dosesof insulin as a form of insulin therapy and/or a meal-aware artificialpancreas. An insulin dosage calculator can take into account glucoselevel at start of eating, a first or higher order derivative of theglucose level at start of eating to determine dosage and timing ofinsulin delivery. Insulin may be delivered at once, in multiple doses orin micro-doses. If additional information about food is obtained fromobject information retrieval subsystem (e.g., drinking a can of regularsoda versus diet soda), this information may be taken into account bythe insulin dosage calculator. For example, if a food item with highsugar content is being consumed, the insulin dosage calculator mayincrease the dosage administered per micro-dosing event or may increasethe number of micro-doses being delivered in a given period of time.

FIG. 19 shows a high level functional diagram of a medication dispensingsystem covered by the current disclosure. A medication dispensing systemmay in part include one or more of the following: a dietary tracking andfeedback system 1902, one or more sensors or sensor units 1900, ameasurement sensor processing unit 1909, a medication dosing calculationunit 1906, one or more measurement sensor units 1904 and a medicationdispensing unit 1908.

In one embodiment of the current disclosure, the medication to beadministered is insulin, the measurement sensor unit 1904 is acontinuous glucose monitor sensor that measures interstitial glucoselevels, medication dispensing unit 1908 is an insulin pump andmedication dosing calculation unit 1906 is the insulin dosagecomputation unit of an automated insulin delivery system (a.k.a.,artificial pancreas).

Each of the elements shown in FIG. 19 might be implemented by suitablestructure. For example, these could be individual hardware elements orimplemented as software structures in a wearable device, an ancillarydevice that communicates with a wearable device, or a server coupledover a network to the ancillary device and/or a wearable device. Someelements might be entirely software and coupled to other elements thatare able to send and/or receive messages to or from the processorexecuting the software. For example, medication dispensing unit 1908might be an embedded hardware system that injects doses or micro-dosesof medication in response to instructions given by a software system andthus would only require minimal processing on-board. It is also possiblethat medication dosing calculation unit 1906 and medication dispensingunit 1908 are housed in the same hardware. Certain elements of FIG. 19may be distributed across multiple software and or hardware elements. Asan example, a portion of medication dosing calculation unit 1906 may beimplemented on one electronic device that is separate from theelectronic device that houses medication dispensing unit 1908, whereasanother portion of medication dosing calculation unit 1906 may beembedded within the same housing as medication dispensing unit 1908.

Dietary tracking and feedback system 1902 may be implemented asdescribed elsewhere herein and may monitor the outputs of one or moresensor unit(s) to determine the actual, probable or imminent start of afood intake event. Upon detection of an actual, probably or imminentstart of a food intake event, or some time thereafter, it may send asignal 1903 to medication dosing calculation unit 1906 to informmedication dosing calculation unit 1906 that an actual, probable orimminent start of a food intake event has been detected. Medicationdosing calculation unit 1906 may use this information to change itsstate to an “imminent meal”, “probable meal” or “meal-in-progress”state.

Upon entering the imminent meal, probable meal or meal-in-progress stateor some time thereafter, medication dosing calculation unit 1906 maycalculate an initial meal medication dosage to be administered and sendone or more messages to medication dispensing unit 1908. Alternativelyor additionally, it may also alter one or more other parameters such asthe target glucose level in response to the detection and signaling ofan imminent, probable or actual start and/or progress of a food intakeevent. Alternatively, the medication dosage to be administered inconnection with the start and/or progress of a food intake event mayhave been pre-configured or calculated ahead of the occurrence of thefood intake event. Upon receiving those messages 1907, medicationdispensing unit 1908 may initiate delivery of the medication.

The medication dispensing unit may deliver the medication all at ones oraccording to a delivery schedule. The delivery schedule may bedetermined and communicated to the insulin delivery system by themedication dosing calculation unit. The delivery schedule may bedetermined upon entering the meal-in-progress state or some timethereafter. The delivery schedule may also be pre-configured ahead ofthe occurrence of the food intake event.

The initial meal medication dosage and/or delivery schedule may coverthe entire anticipated medication dosage for the food intake event.Alternatively, the initial meal medication dosage and/or deliveryschedule may only cover a portion of the entire anticipated medicationdosage for the food intake event, and additional medication dosages areexpected at a later time during or after the food intake event.

Medication dosing calculation unit 1906 may take into account additionalinputs when calculating an initial medication dosage and or initialdelivery schedule. Some inputs may be related to current or recentmeasurements, current or recent user activities and behaviors or otherinformation corresponding to a current or recent state or condition of auser. Other inputs may be related to historical measurements, historicaluser activities and behaviors or other information corresponding to apast state or condition of a user.

Examples of Additional Inputs

Medication dosing calculation unit 1906 may take into account one ormore outputs 1910 from measurement sensor processing unit 1909.Medication dosing calculation unit 1906 may perform additionalprocessing steps on outputs 1910. For example, measurement sensor unit1904 may be a continuous glucose monitor (“CGM”), and outputs 1910 ofmeasurement sensor processing unit 1909 may be interstitial glucoselevel readings. Outputs 1910 may for example be updated every couple ofminutes. Other update frequencies are also possible. Outputs 1910 mayalso be updated continuously. Medication dosing calculation unit 1906may take into account one or more interstitial glucose level readings.For example, medication dosing calculation unit 1906 may take intoaccount the most recent reading. Medication dosing calculation unit 1906may calculate certain parameters that are indicative of changes ininterstitial glucose level readings. For example, medication dosingcalculation unit 1906 may calculate the minimum, mean, maximum, standarddeviation, slope or higher order derivative of interstitial glucoselevel readings over one or more time windows. A time window may span aperiod of time preceding the transition to the meal-in-progress state,span a period of time that includes the transition to themeal-in-progress state, or span a period of time some time after thetransition to the meal-in-progress state. Other or additionalmeasurement sensor units such as heart rate, blood pressure, bodytemperature, hydration level, fatigue level are also possible. Aninsulin delivery system may also take into account a user's currentlocation.

Medication dosing calculation unit 1906 may also take into account otherinputs such as information related to a user's current or recentphysical activity, sleep, stress etc. Medication dosing calculation unit1906 may also take into account personal information such as gender,age, height, weight, etc.

Medication dosing calculation unit 1906 may also take into accountinformation related to a user's medication dosing needs, such as forexample a user's insulin basal rates, a user's insulin-to-carb ratios,and a user's insulin correction factor. This information may be enteredor configured by the user, by a caregiver or by a health record orhealthcare maintenance system. It is also possible that this informationis computed automatically based on historical insulin, carbohydratesintake, blood glucose levels, etc., collected from the patient or fromdevices worn by the patient. Information related to a user's medicationdosing needs may also be derived from historical data collected andstored by the medication dispensing system. For example, the dosage ofmedication (e.g. insulin) delivered by the medication dispensing unit ina period of time preceding the current food intake event. Medicationdosing calculation unit 1906 may take into account medication dosagesdelivered in connection with one or more prior food intake events thatoccurred in the past at or around (e.g., within a specified time window)the same time of day, and/or the same day of the week.

Medication dosing calculation unit 1906 may also take into account themedication still active from previous medication dispensing events, suchas for example the insulin-on-board.

Medication dosing calculation unit 1906 may also include parametersrelated to food intake events that occurred in the past at or around(e.g. within a specified time window) the same time of day, and/or thesame day of the week, and/or at the same location. Medication dosingcalculation unit 1906 may for example look at the duration of past foodintake events, the estimated amounts consumed during past food intakeevents, the average pace of eating during past food intake events, theeating methods of past food intake events, the type of utensils orcontainers used during past food intake events, or the amounts ofcarbohydrates consumed during past food intake events. Other parametersare also possible. Some of these additional parameters (e.g., durationor pace) may be computed by the food intake tracking and feedback systemwithout requiring any user intervention. In other cases, a userintervention, input or confirmation by the user may be necessary.

Medication Dispensing Schedule

Medication dosing calculation unit 1906 may instruct medicationdispensing unit to administer the initial medication dosage all at once,or may specify a delivery schedule for administering the medication. Inone embodiment of the current disclosure, medication dosing calculationunit 1906 computes the medication dosage as well as a schedule for thedelivery of the medication. As an example, medication dosing calculationunit 1906 may determine that 5 units of insulin need to be delivered andmay specify a delivery schedule as follows: 2 units to be administeredimmediately, 1 unit after 2 minutes, 1 unit after 5 minutes and 1 unitafter 7 minutes. This is just one example and other time profilestructures are of course possible.

Medication dosing calculation unit 1906 may communicate both themedication dosage and schedule to medication dispensing unit 1908.Alternatively, medication dosing calculation unit 1906 may manage theschedule and send one or more messages to medication dispensing unit1908 every time medication needs to be administered along with thedosage of medication to be administered.

In a preferred embodiment of the current disclosure, medication dosingcalculation unit 1906 may upon entering the meal-in-progress state orsome time thereafter instruct medication dispensing unit 1908 toinitiate delivery of medication, via the messages 1907. It may forexample instruct medication dispensing unit 1908 to deliver one or moresmall micro-doses of medication.

Additional Dosages and Dosage Adjustments

During the food intake event and/or some time after the food intakeevent, medication dosing calculation unit 1906 may periodically (e.g.,every couple of minutes) or continuously monitor in part one or moreinputs 1905 from measurement sensor processing unit(s) 1909, and/or oneor more inputs 1903 from dietary tracking and feedback system 1902 todetermine if and how much additional medication should be administeredor whether a scheduled medication dispensing should be adjusted. Otherinputs such as inputs described in earlier sections of this disclosuremay also be taken into account.

When computing a medication dosage or medication dosage adjustment,medication dosing calculation unit 1906 may take into account whether ornot a food intake event is in progress. If a food intake event is not orno longer in progress, medication dosing calculation unit 1906 may forexample take into account the time since the end of the last food intakeevent. If a food intake event is in progress, medication dosingcalculation unit 1906 may for example take into account the time elapsedsince the start of the current food intake event, the average or medianpace of eating since the start of the current food intake event, theestimated total amounts consumed since the start of the current foodintake event. Other examples are also possible.

In a specific embodiment of the current invention, medication dosingcalculation unit 1906 may compute and/or adjust, in real time or nearreal time the dosage to be delivered by medication dispensing unit 1908at a specific time based on at least one or more of the following: theactual or probable occurrence of a bite gesture, the actual or probableoccurrence of a minimum number of bites, sips or mouthfuls within aspecified window relative to the specific time of medication dosing, thepace of food intake within a time window relative to the specific timeof medication dosing and/or relative to the start of the food intakeevent exceeding a pre-configured threshold, the amounts consumed sincethe start of the food intake event or in a specified window relative tothe specific time of medication dosing exceeding a pre-configuredthreshold, the duration of the food intake event exceeding apre-configured threshold.

As an example, the medication dosing calculation unit 1906 may computean initial medication dosage to be administered by medication dispensingunit 1908 when the start of food intake is detected. It mayalternatively or additionally determine a dose or micro-dose ofmedication to be delivered each time a bite gesture is detected.Medication dosing calculation unit 1906 may communicate such dosinginformation to medication dispensing unit 1908. The dose or micro-doseto be delivered per bite gesture (e.g. the “insulin-to-bites” ratio) mayvary by user and may further vary based on meal type, time of day, dayof week, location, other detected lifestyle events etc. Medicationdosing calculation unit 1906 may take into account a patient'shistorical food intake data, historical insulin needs and historicalglucose levels when determining the micro-dose to be delivered per bitegesture. Other examples are also possible.

Medication dosing calculation unit 1906 may adjust a dosing andcommunicate such adjustment to medication dispensing unit 1908 based onthe observed absence of one or more characteristics or actions in thecurrent behavior event. For example, medication dosing calculation unit1906 may adjust a dose downwards if a food intake event ends prior tothe assumed, anticipated or predicted end time, if the number of bitesand/or amounts consumed are less than the assumed, anticipated orpredicted amounts consumed, if the pace of eating is slower than theassumed, anticipated or predicted pace of eating. Other examples arealso possible.

Medication dosing calculation unit 1906 may also take into account oneor more recent inputs from measurement sensor processing unit 1909. Forexample, in case medication dosing calculation unit 1906 is an insulindosing unit in an automated insulin delivery system (aka artificialpancreas) and measurement sensor unit 1904 is a CGM, medication dosingcalculation unit 1906 may take into account the value of the most recentinterstitial glucose level reading and/or the change in interstitialglucose level readings over a time window immediately or closelypreceding the current time. If the most recent interstitial glucoselevel reading is below a specified threshold and/or a change ininterstitial glucose level readings is exceeding a specified negativethreshold, medication dosing calculation unit 1906 may determine toadjust the insulin dosage down, or suspend insulin delivery until theinterstitial glucose level readings reaches a second specified thresholdand/or the change in interstitial glucose level readings is no longerexceeding a second specified negative threshold, has turned positive oris exceeding a specified positive threshold.

In some embodiments of the current disclosure, upon detecting of aconcerning measurement sensor processing unit output, medication dosingcalculation unit 1906 may send an alert to the user, one or more of hiscaregivers, a healthcare provider, a monitoring system or to anemergency response system, or to a third party who may have a direct orindirect interest in being informed about the occurrence of suchepisodes.

Likewise, if the most recent interstitial glucose level reading exceedsa specific threshold and/or a change in interstitial glucose levelreadings is exceeding a specified positive threshold, medication dosingcalculation unit 1906 may determine that an additional medication dosageshould be administered, that an already scheduled medication dosageneeds to be adjusted to a larger dosage or delivered at a time earlierthan the currently scheduled time. Medication dosing calculation unit1906 may optionally take into account additional inputs from dietarytracking and feedback system 1902 to compute the additional medicationdosage or medication dosage adjustment. Medication dosing calculationunit 1906 may send one or more messages 1907 to medication dispensingunit 1908 to inform medication dispensing unit 1908 of the additional oradjusted medication dispensing requirements.

Upon detection of an actual or imminent end of a food intake event, orsome time thereafter, dietary tracking and feedback system 1902 may senda signal 1903 to medication dosing calculation unit 1906 to informmedication dosing calculation unit 1906 that an actual or imminent endof a food intake event has been detected. Medication dosing calculationunit 1906 may use this information to change its state to a“no-meal-in-progress” state. In specific embodiments, the medicationdosing calculation unit, when in a no-meal-in-progress state mayperiodically or continuously monitor in part one or more inputs 1905from measurement sensor processing unit(s) 1909, and/or one or moreinputs 1903 from dietary tracking and feedback system 1902 to determineif and how much additional medication should be administered or whethera scheduled medication dispensing should be adjusted. Other inputs suchas inputs described in earlier sections of this disclosure may also betaken into account. When in the no-meal-in-progress state, the frequencyof monitoring and/or updating/adjusting medication dosing may bedifferent from the frequency of monitoring and/or updating/adjustingmedication dosing when in the “meal-in-progress” state. The algorithmsused to determine a medication dosage or medication dosage adjustmentmay also be different between the “meal-in-progress” state and the“no-meal-in-progress” state.

Description of Medication Dosing Learning System

In some embodiments of the current disclosure, medication dosingcalculation unit 1906 may collect, and store data and information aboutfood intake events. In some embodiments, medication dosing calculationunit 1906 may perform additional processing steps on collected data andinformation before storing. Processing steps may be filtering,averaging, applying arithmetical operations, and applying statisticaloperations. Other processing steps are also possible.

Data and information about food intake events might be stored as dataelements in a database that maintains data records about food intakeevents.

Data elements may be event data elements and contain information orparameters that characterize the food intake event. Such information mayinclude, but is not limited to, the event start time, the day of weekthe event occurred, the date of the event, the duration of the event,the event end time, metrics associated with the speed or pace at whichsubject is eating or drinking, metrics associated with the amounts offood or liquid consumed during the event.

Data elements may also be measurement data elements and containinformation or parameters that characterize one or more signals measuredby one or more measurement sensor units 1904 and processed by one ormore measurement sensor processing units 1909. Such information mayinclude, but is not limited to, sensor reading levels corresponding tospecific times in connection with the food intake event, or the average,minimum, or maximum sensor reading levels corresponding to specific timewindows in connection with the food intake event. Specific times mightfor example be the start of the food intake event, periodic orpre-defined points in time during the food intake event, the end of thefood intake event, or periodic or pre-defined times after the foodintake event. Other times are also possible. Specific time windows mightfor example be a duration of time immediately preceding the start of thefood intake event, a duration of time prior to the start of the foodintake event, the duration of the food intake event, a specific durationof time within the food intake event, a duration of time immediatelyfollowing the end of a food intake event or a duration of time some timeafter the end of a food intake event.

In one specific embodiment the medication dosing calculation unit is anautomated insulin delivery system, and the sensor reading levels areinterstitial glucose reading levels obtained from a continuous glucosemonitoring sensor.

Data elements may also be dosage data elements and contain informationor parameters that characterize the medication dosage and deliveryschedule in connection with the food intake event.

Other data elements are also possible. One or more event data elements,one or more measurement data elements and/or one or more dosage dataelements of the same food intake event may be recorded as a singlerecord entry in a database. Event data elements, measurement dataelements and/or dosage data elements may also be recorded as separaterecords in a database. Other data structures consistent with theteachings herein might also be used, or used instead.

Medication dosing calculation unit 1906 may include a processing andanalysis subsystem.

The processing and analysis subsystem may use statistics, machinelearning or artificial intelligence techniques on entries in thedatabase to build a model that recommends an adequate medication dosageand/or delivery schedule. The processing and analysis subsystem may beused to recommend an initial medication dosage, and/or to recommendadditional medication dosage(s) or dosage adjustment(s).

FIG. 20 is an illustrative example of a machine learning system thatmight be used with other elements described in this disclosure. Themachine learning system of FIG. 20 includes a dosage training subsystem2020 and a dosage predictor subsystem 2021. In some embodiments of thepresent disclosure, the machine learning system may include additionalsubsystems or modified versions of the subsystems shown in FIG. 2 .Dosage training subsystem 2020 might use event data elements 2022,measurement data elements 2023 and dosage data elements 2024 as inputs.The dosage training sub-system applies machine learning techniques tobuild a model that recommends an adequate medication dosage and/or amedication dispensing schedule. It might use supervised learningtechniques on one or more entries from the database to train the model.Event data element(s) 2022 and/or measurement data element(s) 2023 mightbe used as features for the model. One or more dosage data elements 2024might be used as labels. Trained model(s) 2025 and/or 2029 is then usedin dosage predictor subsystem 2021 to generate a medication dosagerecommendation and/or medication dispensing recommendation correspondingto a new unlabeled data input 2026.

Medication dosing calculation unit 1906 may include a processing unitand perform additional processing on the data elements and analyze thedata to extract information about the user's eating and drinkingactivities and behaviors, sensor measurements (e.g., glycemic control)and/or medication regimen. Processing may include but is not limited tofiltering, extracting specific data elements, modifying data or dataelements, combining data or data elements. Analysis may also includecomparing specific data elements to data that is stored in a look uptable or database, correlating data elements to data elements that wereobtained at an earlier time and/or from different subjects. Medicationdosing calculation unit 1906 may store raw or processed data in one ormore data storage unit(s). Storage may be temporary or permanent.

In some variations, records in the database may be subdivided intogroups (e.g., based on meal type—breakfast, lunch, dinner, snack) and adifferent model may be used for each subgroup. Alternatively, the samemodel may be used but it may be trained using data from only one or aselective set of subgroups. In other variations, instead of a supervisedmachine learning approach (i.e., where features are specified manually),unsupervised learning may be used instead. With unsupervised learning,the classifier will autonomously generate features from the raw datasetprovided.

Medication dosing calculation unit 1906 may collect, and store data andinformation about other user activities. For example, medication dosingcalculation unit 1906 may collect information or data about a user'sphysical activity, sleep activity, sexual activity. Medication dosingcalculation unit 1906 may also collect and store information about auser's stress, heart rate, blood pressure etc. In some embodiments,medication dosing calculation unit 1906 may perform additionalprocessing steps on collected data and information before storing.Processing steps may be filtering, averaging, applying arithmeticaloperations, and applying statistical operations. Other processing stepsare also possible. Medication dosing calculation unit 1906 may link thisdata and information to one or more food intake events. Data andinformation about food intake events might be stored as data elements ina database that maintains data records about food intake events. Thesedata elements may also be used as inputs to processing and analysissubsystem of medication dosing calculation unit 1906. For example, thesedata elements may be used as additional or alternative event data inputsto a dosage training subsystem. These data elements may for example befeatures for the model.

Medication dosing calculation unit 1906 may also collect inputs such asinformation related to a user's physical activity, sleep, stress etc.Medication dosing calculation unit 1906 may for example compare a user'scurrent or recent physical activity to past physical activity and usethe output of that comparison into the calculation of a medicationdosage.

While not illustrated in detail in FIG. 20 , the machine learningsystem, dosage training subsystem 2020, and dosage predictor subsystem2021 can be implemented using various structural elements. For example,dosage prediction might be implemented using computer hardware, such asa processor, program code memory and program code stored in the programcode memory. This might be a separate embedded unit, or might beimplemented on a processor with memory that is used for other functionsand tasks as well as dosage prediction. This processor and memory mightbe built into a wearable device, a mobile device that communicates witha wearable device, a server that is in communication, directly orindirectly, with a wearable device or sensors, or some combination ofthe above. Other elements might similarly be implemented, such asstorage for event data elements 2022, storage for measurement dataelements 2023 and storage for dosage data elements 2024, program codethat implements the machine learning techniques to build a model thatrecommends an adequate medication dosage and/or a medication dispensingschedule, storage for the model, storage for a database to train themodel, and hardware circuitry needed to communicate messages, such assending a medication dosage recommendation message, a medicationdispensing recommendation message, or a signal to a hardware device thatdispenses medication.

In certain embodiments, the medication dispensing system of FIG. 19 mayoperate without any manual intervention or at least not requiring anymanual intervention. In other embodiments, the medication dispensingsystem of FIG. 19 may require some manual intervention. In one example,medication dosing calculation unit 1906 may calculate a medicationdosage and/or medication delivery schedule but, instead of instructingthe medication dispensing unit 1908 to initiate or schedule delivery ofmedication, it may send a message to the patient, one or more caregiversof the patient, a healthcare professional, a monitoring system, etc. toconfirm the proposed medication dosage and/or medication deliveryschedule. The message can be a text message, a push notification, avoice message, etc., but other message formats are also possible.

In some embodiments, the patient, caregiver, etc. may have an option toalter the proposed medication dosage and/or medication deliveryschedule. Upon receiving a confirmation from the patient, caregiver,etc., medication dosing calculation unit 1906 may send one or moreinstructions to the medication dispensing unit 1908 to initiate orschedule delivery of medication. The instruction(s) may also be sent bya device or unit other than the medication dosing calculation unit 1906.As an example, the instruction(s) may be sent to the medicationdispensing unit 1908 directly from the device on which the patient,caregiver etc. received the message to confirm the medication dosageand/or medication delivery schedule. Other user interventions are alsopossible, such as allowing for a “snooze” function to move a message toa predetermined time in the future.

FIG. 21 is a schematic functional diagram of an exemplary embodiment ofan automated medication dosing and dispensing system 2100 that employsbehavior prediction. The illustrated system 2100 includes various blocksthat represent functional modules, devices or hardware elements, and/orprocessing logic. Moreover, the depicted embodiment of the system 2100includes functional modules and related processing logic that aresimilar to those described above with reference to the exemplary systemshown in FIG. 19 . Accordingly, common reference numbers are used inFIG. 19 and FIG. 21 to indicate similar, identical, or equivalent blocksor signals. In this regard, the system 2100 includes or cooperates withsensors or sensor units 1900 that are designed, configured, and operatedto detect physical movement of the user (more particularly, to detectmovements of specific body parts of the user, such as a wrist, aforearm, a hand). The system 2100 also includes a physical behaviordetection system (implemented as a dietary tracking and feedback system1902), measurement sensor units 1904, a medication dosing calculationunit 1906, a medication dispensing unit 1908, and a measurement sensorprocessing unit 1909 as described above with reference to FIG. 19 .Sensor data 1901 from the sensor units 1900 is processed by the dietarytracking and feedback system, which generates output (represented by thesignal 1903) that serves as an input to the medication dosingcalculation unit 1906. The medication dosing calculation unit 1906generates output (e.g., messages 1907) that is provided to controlcertain functions of the medication dispensing unit 1908. Themeasurement sensor units 1904 generate sensor output (reference number1905) that is provided to the measurement sensor processing unit 1909.Output 1910 from the measurement sensor processing unit 1909 is providedto the medication dosing calculation unit 1906, which calculates dosagesbased on that output 1910 and other factors. In this regard, themeasurement sensor unit(s) 1904 or the measurement sensor processingunit 1909 may serve as a source of data that can be used to supportvarious features, operations, and functions of the system 2100.

The illustrated embodiment of the system 2100 also includes orcooperates with the following components, without limitation: anancillary event detection system 2102; and a dietary prediction system2104. The ancillary event detection system 2102 serves as a source ofdata that generates output used as ancillary inputs 2106 to the dietaryprediction system 2104. In certain embodiments, at least some of theinformation generated by the ancillary event detection system 2102 isused to support additional functions, features, or operations of thesystem, as described in further detail below. For this particularimplementation, the dietary prediction system 2104 also cooperates withthe dietary tracking and feedback system 1902, the medication dosingcalculation unit 1906, and the measurement sensor processing unit 1909.More specifically, the dietary prediction system 2104 may obtain: foodintake event information 2110 from the dietary tracking and feedbacksystem 1902; dosing information 2112 from the medication dosingcalculation unit 1906; and/or sensor measurement information 2114 fromthe measurement sensor processing unit 1909, as depicted in FIG. 21 .Although not represented by a distinct arrow in FIG. 21 , output fromthe sensor units 1900 may also be provided to the dietary predictionsystem 2104. The dietary prediction system 2104 generates predictiveinformation 2116 as an output, which is provided to at least themedication dosing calculation unit 1906. In this regard, the dietaryprediction system 2104 may serve as a source of data that can be used tosupport various features, operations, and functions of the system 2100.Thus, the ancillary event detection system 2102 and the dietaryprediction system 2104 enhance the operation of the “baseline”medication dispensing system described above and shown in FIG. 19 .

The ancillary event detection system 2102 is configured to detectoccurrences of events that can be used to predict whether or not theuser will consume something in the future. Alternatively oradditionally, the ancillary event detection system 2102 is configured togenerate information or data that can be processed (e.g., by the dietaryprediction system) to predict whether or not the user will consumesomething in the future. In contrast, the dietary tracking and feedbacksystem 1902 is designed to detect physical behavior events that“directly” correspond to an eating or drinking event, such as chewing,sipping, biting, movement corresponding to eating with a spoon, fork, orknife, etc. This description uses “ancillary” as a label for detectablephysical behavior events that are not explicitly and directly associatedwith eating or drinking per se (e.g., location data, orientation data,data related to physical and other activities, heart rate data, bloodglucose levels and other biometric data, data related to stress levels,and speech input).

The ancillary event detection system 2102 may include any number ofdevices, sensors, processing logic, functional modules, subsystems,detectors, and/or hardware components as appropriate for the particularembodiment. Although depicted separately in FIG. 21 , the ancillaryevent detection system 2102 may include or leverage one or more of thesensor units 1900 that support the dietary tracking and feedback system1902. The illustrated embodiment of the ancillary event detection system2102 includes or cooperates with at least the following components,without limitation: a GPS system and/or GPS sensors 2122; a healthmonitor with associated sensors 2124, e.g., a device that measures bloodoxygen, blood pressure, body temperature, heart rate, and/or othercharacteristics; and a physical activity tracking device 2126, e.g., awearable or carried device that generates data related to user fitness(number of steps taken, amount and quality of sleep time, distancetravelled during walking, running, or cycling, etc.). An embodiment ofthe ancillary event detection system 2102 may include or cooperate withadditional or alternative devices and components, as appropriate for theparticular implementation of the system 2100.

Gesture recognition methods, physical behavior event detection methods,medication dispensing systems, and other methods and apparatus describedelsewhere herein can be applied to various components of the system 2100depicted in FIG. 21 . The following description focuses on the dietaryprediction system 2104, the dietary tracking and feedback system 1902,and the medication dosing calculation unit 1906, along with variousinteractions between those components.

The dietary tracking and feedback system 1902 detects the actual startor probable start of a food intake event, and provides associated output1903 to the medication dosing calculation unit 1906. The output 1903 mayinclude an indication of the detected food intake event, optionally withadditional characteristics of the food intake event. The dietarytracking and feedback system 1902 may send this information to themedication dosing calculation unit 1906 when it occurs, becomesavailable, or at some time after it occurs or becomes available.Alternatively, the medication dosing calculation unit 1906 mayperiodically query the dietary tracking and feedback system 1902 forsuch information. In some embodiments, the dietary tracking and feedbacksystem 1902 may determine a probability (e.g., a score, a grade, or anymeasured value) that a food intake event is occurring and signal thedetermined probability to the medication dosing calculation unit 1906.

The dietary tracking and feedback system 1902 may also send food intakeevent information 2110 about current or past food intake events to thedietary prediction system 2104. Alternatively, or additionally, thedietary prediction system 2104 may periodically query the dietarytracking and feedback system 1902 for current or past food intake eventsdetected by the dietary tracking and feedback system 1902. The foodintake event information 2110 may include, for example, the date, starttime, end time, duration, and/or the average or median pace of a foodintake event. The food intake event information 2110 may includeparameters such as the detected eating or drinking method, the detectedutensils or container used, the meal type, or parameters that areindicative of amounts or content of foods/liquids consumed. Othercharacteristics or parameters of a detected food intake event may alsobe sent to the dietary prediction system 2104.

The dietary prediction system 2104 may store and process the food intakeevent information 2110 received from the dietary tracking and feedbacksystem 1902, optionally in combination with the ancillary inputs 2106obtained from the ancillary event detection system 2102, to determine alikelihood of the occurrence of a food intake event or start of a foodintake event at a given date and time (or within a given time window) inthe future. Accordingly, the dietary prediction system 2104 considersphysical behavior events that were detected in the past, for purposes ofanticipating or predicting the occurrence of similar or correlatedphysical behavior events that have yet to occur. The predictiveinformation 2116 generated by the dietary prediction system 2104 mayindicate the probability that a meal will start soon, or a set ofprobabilities that a meal will occur within a set of specified timewindows. As an example, the dietary prediction system 2104 may output aprobability or confidence level that a food intake event will start inthe next 15 minutes. In accordance with certain embodiments, the dietaryprediction system outputs such a probability or confidence level whenthere is no meal currently in progress. Each prediction, or the overallset of predictions, may be accompanied by further information such asthe predicted contents of the meal, the predicted number of calories tobe consumed, the predicted pace of eating, the predicted number ofbites, the predicted duration of the meal, the predicted eatingmethod(s) or utensil(s) used, or other information. Each of thesepredictions may or may not be in the form of a probability distributionover the set of possible values.

As an example, the dietary prediction system 2104 may predict that afood intake event is expected to start within the next ten minutes, andthat the confidence level of this prediction is 83%. The dietaryprediction system 2104 may further provide eating method confidencelevels, such as: 12% eating with fork, 75% eating with fingers, 7%eating with spoon, 6% eating with chopsticks. The dietary predictionsystem 2104 may further output a prediction for the meal duration, suchas: 90% probability the meal will last more than three minutes, 50%probability the meal will last more than seven minutes, 10% probabilitythe meal will last more than 13.7 minutes. Different outputs and outputformats are also possible. Furthermore, the predictions that areoutputted by the dietary prediction system 2104 may be updatedperiodically or as more information about the imminent food intakeevent, or other relevant ancillary information, becomes available.

The dietary prediction system 2104 may, for example, process historicalfood intake event data from a user to determine the percentage of timethat the user started a food intake event at a given time of day orwithin a given time window in the past. That percentage measurement canthen be used to determine the likelihood that a start of a food intakeevent will occur for that user at a given time or within a given timewindow in the future. For example, if the dietary tracking and feedbacksystem 1902 provides a user's historical food intake data over a 35 dayperiod, and in 30 of the 35 days, a start of a food intake event wasdetected or occurred in a time window between 6:00 PM and 6:15 PM, thedietary prediction system 2104 could then determine and signal thatthere is an 85.7% probability that the same user will start a foodintake event between 6:00 PM and 6:15 PM on a day in the future. Thisdata could be used in a real-time predictive implementation, where thedietary prediction system 2104 provides to the medication dosingcalculation unit 1906 the probability of a food intake event starting inthe next 15 minutes. Other time intervals are also possible. The dietaryprediction system 2104 could provide this information periodically tothe medication dosing calculation unit 1906, when triggered by anexternal signal, or when queried by the medication dosing calculationunit 1906.

The dietary prediction system 2104 can improve the accuracy of itspredictions by taking into account additional information such ascalendar information (e.g., the day of the week, the season, or themonth). In the above example, there were five days where no food intakeevent was recorded between 6:00 PM and 6:15 PM. If those five days wereall Sundays, the dietary prediction system 2104 could then determinethat there is a close to 100% probability that a user will start a foodintake event between 6:00 PM and 6:15 PM on Mondays through Saturdays,and a close to 0% probability for the same time window on Sundays.

The dietary prediction system 2104 may also improve the accuracy of itspredictions by taking into account any of the ancillary inputs 2106.Examples of ancillary inputs 2106 include, without limitation: locationdata, orientation data, data related to physical and other activities(e.g., walking, running, biking, swimming, sleeping, driving, cooking),heart rate data, blood glucose levels and other biometric data, datarelated to stress levels, and speech input. Ancillary inputs 2106 mayalso include further contextual information such as which people,devices, and noises are nearby. Ancillary inputs 2106 may be associatedwith a particular time or time window preceding a food intake event, atime or time window related to the start, probable start, or imminentstart of a food intake event, a time or time window related to takingplace of a food intake event or a time or time window following a foodintake event. Ancillary inputs 2106 may have a temporal relationshipwith the food intake event, and the parameter of interest for theprediction may be the relative time that elapses between the detectionof one or more ancillary inputs 2106 or a change in one or moreancillary inputs 2106 and the time of the start of a food intake event.For example, an ancillary input 2106 may be location and the feature ofinterest may be the relative time that elapses between the arrival timeat that location and the start of a food intake event. In anotherexample, the ancillary input 2106 may be a physical activity, and thefeature of interest may be the relative time that elapses between theend of the physical activity and the start of a food intake event. Otherexamples are also possible.

The following is an example where the dietary prediction system 2104records location information as one of the ancillary inputs 2106. Thisexample assumes that the dietary tracking and feedback system 1902provides a user's historical food intake data over a 35-day period. In30 of the 35 days, there was a food intake event that began in a timewindow between 6:00 PM and 6:15 PM. This example also assumes that forthe 30 days where there was a food intake occurrence recorded between6:00 PM and 6:15 PM, the user was at home at 6:00 PM, and for the fivedays where there was no start of a food intake occurrence recordedbetween 6:00 PM and 6:15 PM, the user was at work at 6:00 PM. Using thisinformation, the dietary prediction system 2104 indicates at 6:00 PMthat a food intake event is likely to begin in the next 15 minutes,based on the location information provided by the ancillary inputs 2106.If the user is home at 6:00 PM, the dietary prediction system 2104 maydetermine that there is a close to 100% probability that there will be astart of a food intake event for this user in the next 15 minutes. Ifthe user is at work at 6:00 PM, the dietary prediction system maydetermine there is a close to 0% probability that there will be a startof a food intake event for this user in the next 15 minutes.

The dietary prediction system 2104 may further process the percentage ofhistorical occurrences before sending it to the medication dosingcalculation unit 1906. For example, the dietary prediction system 2104may clip, scale, smoothen, or quantize the probability data. The dietaryprediction system 2104 may, for example, use a scale from 1 to 10 torate the probability and communicate the output of the rating to themedication dosing calculation unit 1906. The dietary prediction system2104 may also compute a metric representative of the amount ofhistorical data available to signal a confidence level of theprediction. For example, if the prediction is based on 100 historicaldata points, the confidence level of the prediction would be lowercompared to a scenario where the prediction is based on 10,000historical data points.

In another example, the dietary prediction system 2104 may use data fromthe dietary tracking and feedback system 1902 in combination with theancillary inputs 2106 that provide location information to compute theprobability that a start of a food intake event will occur at a certaintime or within a certain time window after a user left or arrived at aspecific location, based on the historical probability/likelihood. Forexample, the dietary prediction system 2104 can compute the probabilitythat the start of a food intake event will occur in a time window 15 to30 minutes or 30 to 45 minutes after the user arrives home.

In another example, the dietary prediction system 2104 may use data fromthe dietary tracking and feedback system 1902 in combination withancillary inputs 2106 that provide location information toadjust/improve the calculated probability that food intake took place ata certain time or within a certain time window before a user left orarrived at a specific location, based on the historicalprobability/likelihood. For example, the dietary prediction system 2104can compute the probability that the food intake occurred in a timewindow 15 to 30 minutes or 30 to 45 minutes before the user leaves thehouse.

The dietary prediction system 2104 may improve the accuracy of itspredictions by taking into account ancillary inputs 2106 associated withevents that are not food intake events per se, but that may have atemporal relationship to the start or occurrence of a food intake event.In certain embodiments, such events are at least in part detected usinggesture recognition technology based on motion sensor information fromone or more wearable devices. Examples of such detectable eventsinclude, without limitation: a user filling a bowl with cereal; the useropening a refrigerator or a freezer, a cutting motion, a user placing anapkin on their lap, opening a can or other type of container, startinga microwave oven, opening a wrapper, a scooping motion, pouring abeverage, sitting down, moving a chair to table, and setting a tablewith dishes or utensils. In this regard, the dietary prediction system2104 may interface to the ancillary event detection system 2102 (whichuses motion sensor information from one or more wearable devices) todetermine that someone is setting the table. Accordingly, “setting thetable” ancillary event information is sent to the dietary predictionsystem 2104 as an ancillary input 2106. The dietary prediction system2104 may use the data from the dietary tracking and feedback system 1902in combination with the ancillary inputs 2106 to compute the percentageof time that a start of a food intake event occurred within a specifiedtime window following an ancillary event detection, and use thatinformation to predict the probability that the start of a food intakeevent will occur in a specific time window in the future.

As another example, the dietary prediction system 2104 may use data fromthe dietary tracking and feedback system 1902 in combination withancillary inputs 2106 that provide ancillary event information tocompute the probability distribution of the contents of a meal currentlyin progress, based on the historical probability/likelihood given theuser's recent activities. For example, the dietary prediction system2104 can compute the probability that the user is eating an energy bar(after determining, detecting, or predicting that the user has justfinished a workout).

The dietary prediction system 2104 may process data received from thedietary tracking and feedback system 1902, optionally in combinationwith ancillary inputs 2106 to predict other characteristics of a currentor future food intake event. Examples of such predicted characteristicsinclude, without limitation: the predicted duration of the food intakeevent, or metrics indicative of amounts likely to be consumed during thefood intake event. To this end, the dietary prediction system 2104 mayprocess historical food intake event data from a user to determine thepercentage of time that a user started a food intake event at a giventime of day or within a given time window in the past, along withstatistical information about those food intake events to determine theprobability and characteristics of a future food intake event at thesame time or within the same time window. For example, the dietaryprediction system 2104 may determine that a food intake event startingor occurring between 6:00 PM and 6:15 PM has a mean duration of 12minutes. The dietary prediction system 2104 may also analyze thehistorical data to compute median duration, minimum duration, maximumduration, standard deviation, and/or duration corresponding to X %probability. Other statistical metrics are also possible. The dietaryprediction system 2104 may send one or more of those parameters to themedication dosing calculation unit 1906 for consideration. Similarstatistical characteristics may be computed to predict the pace ofeating, the amounts consumed, etc.

FIG. 22 is a state diagram 2200 that applies to an exemplary embodimentof the medication dosing calculation unit 1906. For this particularimplementation, the states depicted in FIG. 22 are based on inputsreceived from the dietary tracking and feedback system 1902 and thedietary prediction system 2104. Thus, the medication dosing calculationunit 1906 may adjust its medication dosing and medication deliveryschedule based on its present meal state. The state diagram 2200 of FIG.22 contemplates the following meal states: No Meal 2202; Imminent Meal2204; Probable Meal 2206; Meal 2208; and Post Meal 2210. Additionaland/or alternative meal states can be defined and supported, asappropriate for the particular implementation of the medication dosingcalculation unit 1906. In FIG. 22 , an arrow leading from one state toanother state represents a change in the current meal state, and anarrow leading from a state back to itself indicates that the currentmeal state does not change.

In accordance with certain embodiments, the medication dosingcalculation unit 1906 is implemented as (or with) an automated closedloop insulin delivery system. When the dietary prediction system 2104predicts the start or occurrence (or the probable start or occurrence)of a food intake event at a certain time or within a certain time windowin the future, the medication dosing calculation unit 1906 may changeits state from the No Meal state 2202 to the Imminent Meal state 2204.Upon transitioning from the No Meal state 2202 to the Imminent Mealstate 2204, or some time thereafter, the medication dosing calculationunit 1906 may increase the basal insulin dosing or lower the bloodglucose target. The medication dosing calculation unit 1906 may takeinto account the current and/or past glucose level reading informationreceived from the measurement sensor processing unit 1909 when decidingif and by how much to increase the basal insulin dosing and/or to whattarget to set the blood glucose target. The medication dosingcalculation unit 1906 may also determine to administer one or more dosesor micro-doses of insulin on a preconfigured or adjustable deliveryschedule. The insulin dosing and delivery schedule may take into accountadditional characteristics about the anticipated future food intakeevent that it receives from the dietary prediction system 2104 (e.g.,predictive information about meal duration or estimated amounts). Themedication dosing calculation unit 1906 may also take into account otherparameters as described elsewhere herein when determining an insulindosing and delivery schedule while in the Imminent Meal state 2204.

Based on one or more inputs received from the dietary tracking andfeedback system 1902, the medication dosing calculation unit 1906 maytransition from the Imminent Meal state 2204 to the Probable Meal state2206, from the Probable Meal state 2206 to the Meal state 2208, or fromthe Meal state 2208 to the Post Meal state 2210. During each statetransition or some time thereafter, the medication dosing calculationunit 1906 may modify its insulin dosing and insulin delivery schedule inan appropriate manner.

During or after a state transition, or while in a given state, themedication dosing calculation unit 1906 may adjust its dosing and/ordelivery schedule based on additional characteristics provided by thedietary tracking and feedback system 1902 or the dietary predictionsystem 2104, or by taking into account other parameters as describedelsewhere herein.

State Transition Example 1

For this example, the medication dosing calculation unit 1906 obtainsthe following information from the dietary tracking and feedback system1902: Probable Meal Start, which is set to a value of “1” if thecriteria to declare a detected probable start of a meal has been met,and is otherwise set to a value of “0”; Meal Start, which is set to avalue of “1” if the criteria to declare a detected start of a meal hasbeen met, and is otherwise set to a value of “0”; and Meal End, which isset to a value of “1” if the criteria to declare a detected end of ameal has been met, and is otherwise set to a value of “0”. For thisexample, the medication dosing calculation unit 1906 obtains thefollowing information from the dietary prediction system 2104: MealPrediction Confidence, which is a confidence level measurement thatindicates the probability that the start of a meal will occur within aspecified period of time from the current time.

Referring to the state diagram 2200, if the medication dosingcalculation unit 1906 is in the No Meal state 2202 and the MealPrediction Confidence is less than a meal prediction thresholdconfidence value, then the No Meal state 2202 is preserved (as indicatedby the loopback arrow 2220). If, however, the Meal Prediction Confidenceis greater than or equal to the meal prediction threshold confidencevalue, the meal state changes from the No Meal state 2202 to theImminent Meal state 2204 (as indicated by the arrow 2222).

If the medication dosing calculation unit 1906 is in the Imminent Mealstate 2204, the Meal Prediction Confidence is greater than or equal tothe meal prediction threshold confidence value, and Probable Meal Startis set to “0”, then the Imminent Meal state 2204 is preserved (asindicated by the loopback arrow 2224). If the medication dosingcalculation unit 1906 is in the Imminent Meal state 2204, the MealPrediction Confidence is less than the meal prediction thresholdconfidence value, and Probable Meal Start is set to “0”, then the mealstate returns to the No Meal state 2202 (as indicated by the arrow2226). If, however, Probable Meal Start is set to “1”, then the mealstate changes from the Imminent Meal state 2204 to the Probable Mealstate 2206 (as indicated by the arrow 2228).

If the medication dosing calculation unit 1906 is in the Probable Mealstate 2206, the amount of time spent in the Probable Meal state 2206 isless than or equal to a predetermined probable meal timeout period, andMeal Start is set to “0”, then the Probable Meal state 2206 is preserved(as indicated by the loopback arrow 2230). If the medication dosingcalculation unit 1906 is in the Probable Meal state 2206 and the amountof time spent in the Probable Meal state 2206 is greater than theprobable meal timeout period, then the meal state returns to the No Mealstate 2202 (as indicated by the arrow 2232). If, however, the amount oftime spent in the Probable Meal state 2206 is less than or equal to theprobable meal timeout period, and Meal Start is set to “1”, then themeal state changes from the Probable Meal state 2206 to the Meal state2208 (as indicated by the arrow 2234).

If the medication dosing calculation unit 1906 is in the Meal state2208, and Meal End is set to “0”, then the Meal state 2208 is preserved(as indicated by the loopback arrow 2236). If, however, Meal End is setto “1”, then the meal state changes from the Meal state 2208 to the PostMeal state 2210 (as indicated by the arrow 2238).

If the medication dosing calculation unit 1906 is in the Post Meal state2210, and the amount of time spent in the Post Meal state 2210 is lessthan or equal to a designated post meal timeout period, then the PostMeal state 2210 is preserved (as indicated by the loopback arrow 2240).If, however, the amount of time spent in the Post Meal state 2210 isgreater than the post meal timeout period, then the meal state changesfrom the Post Meal state 2210 to the No Meal state 2202 (as indicated bythe arrow 2242).

State Transition Example 2

For this example, the medication dosing calculation unit 1906 obtainsthe following information from the dietary tracking and feedback system1902: Detected Meal Start Confidence, which is a confidence levelmeasurement that indicates the probability that the start of a meal hasoccurred; and Detected Meal End Confidence, which is a confidence levelmeasurement that indicates the probability that the end of a meal hasoccurred. For this example, the medication dosing calculation unit 1906obtains the following information from the dietary prediction system2104: Meal Prediction Confidence, as described above for the StateTransition Example 1.

Referring to the state diagram 2200, if the medication dosingcalculation unit 1906 is in the No Meal state 2202 and the MealPrediction Confidence is less than the meal prediction thresholdconfidence value, then the No Meal state 2202 is preserved (as indicatedby the loopback arrow 2220). If, however, the Meal Prediction Confidenceis greater than or equal to the meal prediction threshold confidencevalue, the meal state changes from the No Meal state 2202 to theImminent Meal state 2204 (as indicated by the arrow 2222).

If the medication dosing calculation unit 1906 is in the Imminent Mealstate 2204, the Meal Prediction Confidence is greater than or equal tothe meal prediction threshold confidence value, and the Detected MealStart Confidence is less than or equal to a probable meal startconfidence threshold value, then the Imminent Meal state 2204 ispreserved (as indicated by the loopback arrow 2224). If the medicationdosing calculation unit 1906 is in the Imminent Meal state 2204, theMeal Prediction Confidence is less than the meal prediction thresholdconfidence value, and the Detected Meal Start Confidence is less than orequal to the probable meal start confidence value, then the meal statereturns to the No Meal state 2202 (as indicated by the arrow 2226). If,however, the Detected Meal Start Confidence is greater than or equal tothe probable meal start confidence threshold, then the meal statechanges from the Imminent Meal state 2204 to the Probable Meal state2206 (as indicated by the arrow 2228).

If the medication dosing calculation unit 1906 is in the Probable Mealstate 2206, the amount of time spent in the Probable Meal state 2206 isless than or equal to a predetermined probable meal timeout period, andthe Detected Meal Start Confidence is less than a meal start confidencethreshold value, then the Probable Meal state 2206 is preserved (asindicated by the loopback arrow 2230). If the medication dosingcalculation unit 1906 is in the Probable Meal state 2206 and the amountof time spent in the Probable Meal state 2206 is greater than theprobable meal timeout period, then the meal state returns to the No Mealstate 2202 (as indicated by the arrow 2232). If, however, the amount oftime spent in the Probable Meal state 2206 is less than or equal to theprobable meal timeout period, and the Detected Meal Start Confidence isgreater than or equal to the meal start confidence threshold value, thenthe meal state changes from the Probable Meal state 2206 to the Mealstate 2208 (as indicated by the arrow 2234).

If the medication dosing calculation unit 1906 is in the Meal state2208, and the Detected Meal End Confidence is less than a meal endconfidence threshold value, then the Meal state 2208 is preserved (asindicated by the loopback arrow 2236). If, however, the Detected MealEnd Confidence is greater than or equal to the meal end confidencethreshold value, then the meal state changes from the Meal state 2208 tothe Post Meal state 2210 (as indicated by the arrow 2238).

If the medication dosing calculation unit 1906 is in the Post Meal state2210, and the amount of time spent in the Post Meal state 2210 is lessthan or equal to a designated post meal timeout period, then the PostMeal state 2210 is preserved (as indicated by the loopback arrow 2240).If, however, the amount of time spent in the Post Meal state 2210 isgreater than the post meal timeout period, then the meal state changesfrom the Post Meal state 2210 to the No Meal state 2202 (as indicated bythe arrow 2242).

In certain embodiments, a medication dosing and dispensing system mayinitiate one or more of the following when the dietary prediction system2104 predicts the start or occurrence, or the probable start oroccurrence, of a food intake event at a certain time or within a certaintime window in the future: interacting with the user to provideinformation or a reminder; interacting with the user to prompt for userinput; sending a message to a remote computer system; sending a messageto another person; sending a message to the user.

In certain embodiments, one or more outputs of the dietary predictionsystem 2104 may be sent to the measurement sensor unit 1904 and/or themeasurement sensor processing unit 1909. When the dietary predictionsystem 2104 predicts a future or imminent start of a food intake event,the output of the dietary prediction system 2104 may be used to initiateone or more of the following, without limitation: (1) activateadditional circuitry such as, for example, activating an additionalsensor or a data recording mechanism; (2) activate a higher performancemode of one or more circuit or components of the system; (3) adjust theamount of power supplied to a circuit or component of the system; (4)adjust the latency of a communication channel, such as increasing thesensor read out frequency or update rate at which sensor data istransferred from the measurement sensor unit 1904 to the measurementsensor processing unit 1909, or increasing the sensor read out frequencyor update rate at which outputs of the measurement sensor processingunit 1909 are sent to the medication dosing calculation unit 1906; (5) achange in sensor sampling rate, such as an increased or decreasedsampling rate of one or more sensors in the measurement sensor units1904.

An end of a food intake event might be detected by considering whether acertain time has expired since a last bite or sip movement or when otherdata (metadata about the wearer, motion-detection sensor data, and/orhistorical data of the wearer, or a combination of those) indicates theend. Based on these factors, the system makes a determination that afood intake event is not likely and then changes the state of theelectronic device to an inactive monitoring state, possibly a lowerpower mode. The lower power mode might be implemented by reducing thesampling rate of an accelerometer and/or gyroscope, powering down thegyroscope, reducing the update rate at which sensor data is transferredfrom the electronic device (such as the electronic device 218 shown inFIG. 2 ) to the support device (such as the mobile device 219 shown inFIG. 2 ), or compressing the data before transferring the data from thesensing electronic device to the support electronic device.

The medication dispensing system 2100 and its dietary tracking andfeedback system 1902 were described previously (with particularreference to FIG. 19 and FIG. 21 ). In accordance with certainembodiments of the system 2100, one or more outputs from systems thatare “external” to the physical behavior tracking components orprocessing logic are used to determine the confidence levels associatedwith detected events and/or to reduce the time required to declare thata physical behavior event (e.g., a dietary event) has been detected. Inthis regard, FIG. 21 includes three arrows drawn with dashed lines.These dashed arrows represent additional information that can beobtained and processed by the dietary tracking and feedback system 1902.Although other sources of external data can be received and processed bythe dietary tracking and feedback system 1902, the illustratedembodiment obtains any or all of the following, without limitation: oneor more of the ancillary inputs 2106 provided by the ancillary eventdetection system 2102; predictive information 2116 generated by thedietary prediction system 2104; and sensor data 2140 generated by themeasurement sensor processing unit 1909.

In accordance with this embodiment, the dietary tracking and feedbacksystem 1902 receives additional information from the measurement sensorprocessing unit 1909, the dietary prediction system 2104, and/or theancillary event detection system 2102. As an example, the dietarytracking and feedback system 1902 may process such additionalinformation to adjust the confidence level that a food intake event isoccurring or to accelerate its decision that a food intake event isoccurring or is likely occurring.

As another example, when the dietary prediction system 2104 predicts animminent food intake event, it may send this information to the dietarytracking and feedback system 1902. Moreover, the dietary predictionsystem 2104 may output a probability or confidence level that a foodintake event will start in the next 15 minutes. In certain scenarios,the dietary prediction system 2104 may output such a probability orconfidence level when there is no meal in progress. Each prediction, orthe overall set of predictions, may be accompanied by furtherinformation such as the predicted contents of the meal, the predictednumber of calories to be consumed, the predicted pace of eating, thepredicted number of bites, the predicted duration of the meal, thepredicted eating method(s) or utensil(s) used, or other information.Each of these predictions may or may not be in the form of a probabilitydistribution over the set of possible values. This data can be sent tothe dietary tracking and feedback system 1902 for appropriate handling.The predictions that are provided by the dietary prediction system 2104may be updated periodically or as more information about the imminentfood intake event, or other relevant ancillary information, becomesavailable.

As mentioned previously, the dietary tracking and feedback system 1902detects the actual start or the probable start of a food intake event,based at least in part on data received from the sensor units 1900. Incertain embodiments, the dietary tracking and feedback system 1902 maydetermine a probability that a food intake event is occurring, andprovide the probability information to the medication dosing calculationunit 1906. When making a determination of whether or not a probable oractual start of a food intake event has occurred, and/or when computingthe probability that a food intake event is occurring, the dietarytracking and feedback system 1902 may take into account any amount ofpredictive information 2116 obtained from the dietary prediction system2104.

As another example, if the dietary tracking and feedback system 1902determines with a 65% probability or confidence level that a food intakeevent is occurring, and the dietary prediction system 2104 predictedwith a high probability that a food intake event would occur around thistime, then the dietary tracking and feedback system 1902 can upwardlyadjust its confidence level that a food intake event is currentlyoccurring. Similarly, if the dietary prediction system 2104 predicted avery low probability that a food intake event would occur around thistime, then the dietary tracking and feedback system 1902 can downwardlyrevise its confidence level. In certain embodiments, the dietarytracking and feedback system 1902 may adjust the requirements orcriteria used to determine that a food intake event is occurring. Forexample, the dietary tracking and feedback system 1902 may reduce orincrease the number of bite gesture detections required to declare thata meal is occurring. The dietary tracking and feedback system 1902 maytake into account other predictive information 2116 output by thedietary prediction system 2104 when declaring food intake events orcharacteristics of food intake events, and such consideration can impactthe manner in which the dietary tracking and feedback system 1902influences the operation of the medication dosing calculation unit 1906.

In certain embodiments, the measurement sensor processing unit 1909sends sensor data 2140 or sensor-related signals to the dietary trackingand feedback system 1902. The sensor data 2140 or signals may indicatethat a meal event is occurring. For example, if the measurement sensorunit 1904 is a CGM unit, the measurement sensor processing unit 1909 mayinclude logic that analyzes signals received from the measurement sensorunit 1904 and looks for certain patterns, such as a rise in CGMreadings, or a change in first or higher order derivative of CGMreadings to determine if a food intake event is likely occurring,optionally with an associated probability or confidence level. Otherpatterns are also possible. The measurement sensor processing unit 1909may send this information (e.g., sensor data 2140) for use as an inputto the dietary tracking and feedback system 1902.

The dietary tracking and feedback system 1902 may also determine aprobability that a food intake event is occurring, and provide theprobability to the medication dosing calculation unit 1906. When makinga determination of whether or not a probable or actual start of a foodintake event has occurred, and/or when computing the probability that afood intake event is occurring, the dietary tracking and feedback system1902 may take into account sensor data 2140 obtained from themeasurement sensor processing unit 1909. As an example, if the dietarytracking and feedback system 1902 determines with a 35% probability orconfidence level that a food intake event is occurring, and themeasurement sensor processing unit 1909 also detects with a highprobability that a food intake event is occurring and provides thisinformation to the dietary tracking and feedback system 1902, then thedietary tracking and feedback system 1902 can upwardly adjust itsconfidence level associated with the determination of the food intakeevent. Similarly, if the measurement sensor processing unit 1909 doesnot indicate a food intake event, the dietary tracking and feedbacksystem 1902 may downwardly revise the associated confidence level.Thereafter, the confidence level can be adjusted upwards or downwards asappropriate.

In certain embodiments, the dietary tracking and feedback system 1902may take into account information, data, or measurements provided by theancillary event detection system. Thus, any of the ancillary inputs 2106can also be utilized by the dietary tracking and feedback system 1902.The dietary tracking and feedback system 1902 can process the ancillaryinputs 2106 (along with other information) to determine if a food intakeevent is occurring or likely occurring, and the associated probabilityor confidence level.

FIG. 23 is a simplified block diagram representation of an embodiment ofa gesture-informed physical behavior event detector system 2300. Thedepicted embodiment of the system 2300 includes one or more sensor units2302, a gesture recognizer unit 2304, and a physical behavior eventdetector 2306. The sensor units 2302 generate one or more sensor datastreams 2310, which can be provided to the gesture recognizer unit 2304.A sensor unit 2302 can be realized as any of the following, withoutlimitation: an accelerometer; a gyroscope; or a magnetometer. Althoughnot always required, a sensor unit 2302 can be embedded in a wearabledevice, component, or article. For example, a wearable device may beworn around the wrist (e.g., an activity tracker or smart watch), aroundthe finger (e.g., a ring), or attached to or worn on a different part ofthe arm or hand.

The sensor data streams 2310 are provided to the gesture recognizer unit2304 for processing. To this end, sensor data communicated via thesensor data streams 2310 may be sent in raw format. Alternatively, asensor unit 2302 may perform some processing (e.g., filtering,compression, or formatting) on raw sensor data before sending theprocessed sensor data to the gesture recognizer unit 2304. The gesturerecognizer unit 2304 analyzes the incoming sensor data and converts theincoming sensor data into a stream of corresponding gestures 2314. Thegesture recognizer unit 2304 may use one or more ancillary inputs 2316to aid in the gesture determination process. Nonlimiting examples of anancillary input include: time of day; the probability of a specificgesture occurring based on statistical analysis of historical gesturedata for that user; geographical location; heart rate; otherphysiological sensor inputs. Other ancillary inputs are also possible.

FIG. 24 is a diagram that schematically depicts output of the gesturerecognizer unit 2304 over time, in accordance with one example. When thegesture recognizer unit 2304 detects a gesture, it generates and sendsan output signal that contains at least one of the following: thegesture type (if the gesture recognizer unit 2304 only detects one typeof gesture, then this information may be omitted or replaced with asimple binary output signal); the confidence level or probability thatthe gesture type was determined correctly; and additional metadata suchas, for example, the timestamp or the duration of the detected gesture.In this regard, one output 2402 depicted in FIG. 24 corresponds to adetected bite gesture having a probability of 0.55 and a duration of 1.1seconds. Another output 2406 indicates a detected sip gesture having aprobability of 0.55 and a duration of 1.1 seconds. Another output 2410represents a detected inhale gesture having a probability of 0.55 and aduration of 6.1 seconds.

FIG. 25 is a diagram that schematically depicts output of the gesturerecognizer unit 2304 over time, in accordance with another example. Theoutput shown in FIG. 25 is formatted differently than the output shownin FIG. 24 (other output formats are also possible). In contrast to theexample shown in FIG. 24 , the output depicted in FIG. 25 includesconfidence measurements for different possible gestures, along withcorresponding metadata, e.g., the duration of the detected gesture.Although not always required, the implementation contemplated by FIG. 25considers the following gesture types: bite; sip; and inhale.Accordingly, the output for each detected gesture includes threecorresponding confidence measurements—one for a bite gesture, one for asip gesture, and one for an inhale gesture. The example of FIG. 25 alsoprovides a fourth confidence measurement corresponding to “other” or an“unknown” gesture. In this regard, one output 2502 depicted in FIG. 25corresponds to a detected gesture having a duration of 2.3 seconds. Theoutput 2502 indicates the following confidence measurements: biteconfidence=0.83; sip confidence=0.02; inhale confidence=0.01; “othergesture” confidence=0.14. Accordingly, there is a very high probabilitythat the output 2502 corresponds to a bite gesture. Another output 2506identifies the following confidence measurements: bite confidence=0.23;sip confidence=0.72; inhale confidence=0.01; “other gesture”confidence=0.04. Thus, there is a very high probability that the output2506 corresponds to a sip gesture.

Referring again to FIG. 23 , the output of the gesture recognizerunit—the detected gestures 2314—are sent to event detector 2306. Theevent detector 2306 analyzes the incoming stream of gestures 2314 todetermine if the start of an event of interest has occurred, whether anevent is ongoing, whether an event has ended, or the like. For example,if the event detector 2306 is implemented as a meal detector intended tocapture eating/drinking events, it will be suitably configured todetermine if the start of a meal has occurred, if a meal is in progress,or if a meal has ended. Other examples of detectable physical behavioror activities (e.g., auxiliary activities) include, without limitation:drinking; smoking; brushing teeth; combing hair; talking on the phone;driving a car; inhaling or injecting a medication; and activitiesrelated to hand hygiene or personal hygiene.

The sensor unit(s) 2302, the gesture recognizer unit 2304, and the eventdetector 2306 may all be housed in the same piece of hardware or may bedistributed across multiple pieces of hardware. For example, the sensorunit(s) 2302 may be housed in a wearable device that communicateswirelessly with a second electronic device that houses the gesturerecognizer unit 2304 and the event detector 2306. The second electronicdevice could, for example, be a mobile phone, a medical device such asan insulin pump, an electronic patient health monitor, or the like.

In certain embodiments, the sensor unit(s) 2302, the gesture recognizerunit 2304, and the event detector 2306 are all embedded within awearable device. The wearable device communicates wirelessly with asecond electronic device and the output of the event detector 2306 issent wirelessly to the second electronic device. As mentioned above, thesecond electronic device may be implemented as any of the following,without limitation: a mobile phone, a medical device such as an insulinpump, a patient monitor, or the like. In accordance with an alternatearrangement, multiple sensor units 2302 are used and the sensor units2302 are distributed across at least two wearable devices. Otherconfigurations and partitioning of components across multiple hardwareplatforms are also possible.

The event detector 2306 obtains the incoming stream of gestures 2314,processes the gestures 2314, and makes a determination about an eventstate or change in event state based on a review of the receivedgestures 2314. The event detector 2306 can process the received gestures2314 to generate a suitably formatted output 2320 that identifies anevent state or a change in an event state. The output 2320 can beprovided to another system, device, and/or processing logic. The eventdetector 2306 may use a variety of parameters and decision criteria todetermine an event state or a change in event state.

As mentioned above, the event detector 2306 may be implemented as a mealdetector. In this context, a meal detector may be configured to have thefollowing states, without limitation: no meal in progress; imminentmeal; probable meal in progress; meal in progress; post meal. Differentmeal states and state transitions were described above with reference toFIG. 22 , and the related description may also apply to animplementation of the event detector 2306.

FIG. 26 is a state diagram 2600 that applies to an exemplary embodimentof the event detector 2306. For this particular implementation, thestates depicted in FIG. 26 are determined based on the gestures 2314received from the gesture recognizer unit 2304. The state diagram 2600of FIG. 26 contemplates the following states: No Eating In Progress2602; Drinking In Progress 2604; Eating In Progress 2606; and Meal InProgress 2608. Additional and/or alternative states can be defined andsupported, as appropriate for the particular implementation of the eventdetector 2306. In FIG. 26 , an arrow leading from one state to anotherstate represents a change in the current meal state, and an arrowlooping from a state back to itself indicates that the current statedoes not change.

Based on one or more gestures 2314 received from the gesture recognizerunit 2304, the event detector 2306 may transition from one state toanother, while reporting its current state in the form of the output2320. This example assumes that the event detector 2306 maintains binaryindicators for “Snacking” and “Drinking” and “Meal”, where a value of“0” refers to a negative condition and a value of “1” refers to apositive condition. Accordingly, the No Eating In Progress state 2602 isindicated when Snacking=0, Drinking=0, and Meal=0. If the event detector2306 is in the No Eating In Progress state 2602 and no bite gesture isdetected, then the No Eating In Progress state 2602 is preserved (asindicated by the loopback arrow 2614). If a sip gesture is detected,then the state changes from the No Eating In Progress state 2602 to theDrinking In Progress state 2604 (as indicated by the arrow 2616). If abite gesture is detected, then the state changes from the No Eating InProgress state 2602 to the Eating In Progress state 2606 (as indicatedby the arrow 2618).

The Drinking In Progress state 2604 is indicated when Snacking=0,Drinking=1, and Meal=0. If the event detector 2306 is in the Drinking InProgress state 2604 and no sip gesture is detected within a specifiedperiod of time (e.g., within the last five minutes), then the statechanges from the Drinking In Progress state 2604 to the No Eating InProgress state 2602 (as indicated by the arrow 2620). If a bite gestureis detected while the event detector 2306 is in the Drinking In Progressstate 2604, then the state changes from the Drinking In Progress state2604 to the Eating In Progress state 2606 (as indicated by the arrow2622). For this embodiment, transitioning from the Drinking In Progressstate 2604 to the Eating In Progress state 2606 results in the currentsip count being carried over into the eating event. The sip count iscarried over such that it can be utilized (along with detected bites)when determining whether to remain in the Eating In Progress state 2606or change to a different state. Thus, counting of sips and bites maycontinue while in the Eating In Progress state 2606.

The Eating In Progress state 2606 is indicated when Snacking=1,Drinking=0, and Meal=0. If the event detector 2306 is in the Eating InProgress state 2606, a bite gesture or a sip gesture is detected, anddesignated meal qualification criteria is not met, then the eventdetector 2306 remains in the Eating In Progress state (as indicated bythe loopback arrow 2624). If the event detector 2306 is in the Eating InProgress state 2606, a bite gesture or a sip gesture is detected, andthe designated meal qualification criteria is met, then the statechanges from the Eating In Progress state 2606 to the Meal In Progressstate 2608 (as indicated by the arrow 2626). The meal qualificationcriteria may include any number of rules or conditions that must besatisfied before the event detector declares that a meal is in progress.The following meal qualification criteria is utilized for thisparticular example: at least ten bite gestures detected at a median paceof at least 0.8 bites per minute. It should be appreciated that theparticular meal qualification criteria may vary from one implementationof the event detector 2306 to another, as appropriate for theembodiment, and as appropriate for the type of event(s) underconsideration. If the event detector 2306 is in the Eating In Progressstate 2606 and no bite gesture or sip gesture is detected within aspecified period of time (e.g., within the last ten minutes), then thestate changes from the Eating In Progress state 2606 to the No Eating InProgress state 2602 (as indicated by the arrow 2630).

The Meal In Progress state 2608 is indicated when Snacking=0,Drinking=0, and Meal=1. If the event detector 2306 is in the Meal InProgress state 2608 and a bite gesture or a sip gesture is detected,then the event detector 2306 remains in the Meal In Progress state (asindicated by the loopback arrow 2634). If the event detector 2306 is inthe Meal In Progress state 2608 and no bite gesture or sip gesture isdetected within a specified period of time (e.g., within the last tenminutes), then the state changes from the Meal In Progress state 2608 tothe No Eating In Progress state 2602 (as indicated by the arrow 2636).

FIG. 26 illustrates states and transitions for one exemplaryimplementation of the event detector 2306, which is configured as a mealdetector. In practice, a meal detector can consider parameters such as:the number of gestures classified as bite gestures; the confidence levelof bites gestures; inter-bite gesture spacing; elapsed time sincedetection of the first bite gesture; elapsed time since detection of thelast bite gesture; and estimated amounts consumed. Other parameters arealso possible.

The decision criteria for determining when to change from a ProbableMeal In Progress state to a Meal In Progress state may include, withoutlimitation: reaching or exceeding a specified bite gesture countthreshold with a specified inter-bite gesture spacing maximum/time out;reaching or exceeding a specified bite gesture count threshold whereeach bite reaches or exceeds a specified bite gesture classificationconfidence level with a specified inter-bite gesture spacingmaximum/timeout; reaching or exceeding a specified bite gesture countthreshold within a specified time window since detection of the firstbite gesture. Other criteria are also possible.

The decision criteria for determining when to change to a Post Mealstate or a No Meal In Progress state may include, without limitation:elapsed time since detection of the last bite gesture exceeding aspecified time threshold; elapsed time since entering the current stateexceeding a specified time threshold. Other criteria are also possible.

A meal detector may signal a change from the No Meal In Progress stateto the Meal In Progress state when the state change has occurred (“pushapproach”). Alternatively, a meal detector may make a statedetermination only when being queried or on a fixed predeterminedschedule (“pull approach”). For example, a system external to the mealdetector may request the current meal detector state every two minutes.

In certain embodiments, the event detector 2306 may output a probabilityor confidence level associated with a state determination. Suchprobability or confidence level may be updated periodically or whenqueried.

The event detector 2306 may be a meal detector and the meal detector mayprocess a sequence of bite gestures, optionally along with theconfidence level that the gesture was correctly classified as a bitegesture by the gesture recognizer unit 2304 to determine a probabilitythat a meal has started or is in progress. The meal detector mayrecompute this probability periodically on a fixed schedule, whenqueried by another module or system, or when one or more new gestureshave been received from the gesture recognizer unit 2304. Moreover, theoutput 2320 of the event detector 2306 may be a probability that isindicative of the overall probability that an event has started or isongoing.

As shown in FIG. 23 , the event detector 2306 may use ancillary inputs2324 to make a state determination or compute a state probability. Forexample, if the event detector 2306 is implemented as a meal detector,then the event detector 2306 (or a system interfacing with the eventdetector 2306) may derive the probability that a user is eating from thehistorical eating records from the user, and the meal detector may takethis probability into account when determining whether or not to declarethe Meal In Progress state. As an example, if the historical probabilitythat a user is eating is 90% (suggesting that in nine out of ten casesin the past, the user was determined to be eating at this time or withina time window around this time in the past), the meal detector may use alower bite count threshold to change to the Meal In Progress state. Incontrast, if the historical probability that the user is eating is only10% (suggesting that only in one out of ten cases in the past, the userwas determined to be eating at this time or within a time window aroundthis time in the past), the meal detector may decide to use a higherbite count threshold. In case of a probabilistic meal detector, the mealdetector may weigh the probability that a meal has started or is inprogress with the historical probability.

Heart rate may be another ancillary input 2324. Blood glucose readings(or blood sugar readings derived from interstitial fluid) and/or changesin blood glucose readings (or blood sugar readings derived frominterstitial fluid) may be another ancillary input 2324. An ancillaryinput 2324 may be an input provided by the user, such as a confirmationthat the user is eating, or it may be a measurement or data provided byan electronic device, a sensor component, or the like.

The event detector 2306 may also take into account inputs from anexternal event prediction system (such as the dietary prediction system2104 described above with reference to FIG. 21 ) to make a state orstate change determination or compute a state probability. If theexternal event prediction system is a meal prediction system, and themeal prediction system informs the event detector 2306 that there is ahigh likelihood that the user is about to start eating, the eventdetector 2306 may use more aggressive criteria to declare the Meal InProgress state (e.g., lower bite count threshold) or boost the computedMeal In Progress state probability. If the external event predictionsystem is a sleep prediction system, and the sleep prediction systeminforms the event detector 2306 that there is a high likelihood that theuser is sleeping, the event detector 2306 may use more conservativecriteria to declare the Meal In Progress state (e.g., increase bitecount threshold) or reduce the computed Meal In Progress stateprobability. If an event prediction system predicts that the likelihoodof an event occurring is low, the confidence levels of the gestures 2314considered by the event detector 2306 may be reduced by an absoluteamount or a proportional factor.

FIG. 27 is a simplified high-level functional diagram of certaincomponents of a medication dispensing system 2700 that employs physicalbehavior detection (in the form of a dietary/meal tracking and feedbacksystem). The illustrated system 2700 includes various blocks thatrepresent functional modules, devices or hardware elements, and/orprocessing logic. Moreover, the depicted embodiment of the system 2700includes functional modules and related processing logic that aresimilar to those described above with reference to the exemplary systemsshown in FIG. 19 and FIG. 21 . Accordingly, common reference numbers areused in FIGS. 19, 21, and 27 to indicate similar, identical, orequivalent blocks or signals. In this regard, the system 2700 includesor cooperates with sensor units 1900, the dietary tracking and feedbacksystem 1902, at least one measurement sensor 2701 (e.g., a measurementsensor unit 1904 as depicted in FIG. 21 ) that generates sensor dataindicative of a physiological characteristic of the user, and ameasurement sensor processor 2702 (e.g., a measurement sensor processingunit 1909 as depicted in FIG. 21 ) that processes the sensor datagenerated by the measurement sensor 2701. For clarity and ease ofillustration, the other components and interconnections shown in FIG. 21are omitted from FIG. 27 . It should be appreciated that the componentsdepicted in FIG. 27 are suitably configured, arranged, and operated forcompatibility with the arrangement shown in FIG. 21 .

As mentioned above, sensor data 1901 from the sensor units 1900 isprocessed by the dietary tracking and feedback system 1902, whichgenerates an output associated with a detected physical behavior event,e.g., a meal event. The measurement sensor 2701 generates sensor output2703 that is provided to the measurement sensor processor 2702. Outputof the measurement sensor processor 2702 is utilized as needed. Forexample, the output of the measurement sensor processor 2702 can beprovided to the medication dosing calculation unit 1906 (see FIG. 21 ).In certain implementations, the output of the measurement sensorprocessor 2702 can be provided to the dietary tracking and feedbacksystem 1902 as an input to consider when determining whether agesture-based physical behavior event of interest has occurred or islikely to occur, as described previously.

In certain deployments of the system 2700, the measurement sensor 2701includes or is realized as a continuous glucose monitor or a continuousglucose sensor device. A continuous glucose monitor works well when thedetected occurrence of the gesture-based physical behavior eventcorresponds to a food intake event. In such an embodiment, themedication can be insulin, and the system calculates and dispenses adosage of insulin in response to the detected food intake event and inresponse to the user's blood glucose level as measured by the continuousglucose monitor.

In accordance with the embodiment presented here, output 2704 of thedietary tracking and feedback system 1902 may be provided to themeasurement sensor unit 1904 and/or to the measurement sensor processingunit 1909. In turn, certain functions, features, settings, or operationsof the measurement sensor unit 1904 and/or the measurement sensorprocessing unit 1909 can be adjusted, varied, or otherwise modifiedbased on the output 2704 received from the dietary tracking and feedbacksystem 1902.

For example, when the dietary tracking and feedback system 1902 detectsthe actual or probable start of an event of interest, it may send asuitably formatted output 2704 or signal to the measurement sensor unit1904 and/or the measurement sensor processing unit 1909. Upon receivingthe output 2704, or some time thereafter, the measurement sensor unit1904 and/or the measurement sensor processing unit 1909 may initiate anappropriate state change. Such a state change may include any of thefollowing, without limitation: (1) activating a higher performance modeof one or more circuits or components; (2) supplying additional power toone or more circuits; (3) increasing the sampling rate of themeasurement sensor unit 1904; (4) reducing latency of a communicationchannel such as an increased sensor read out frequency, or a reducedlatency in communication between the measurement sensor processing unit1909 and the medication dosing calculation unit 1906 (see FIG. 21 ); (5)increasing buffer space allocated for data storage. Other actions arealso possible.

Equivalently, when the dietary tracking and feedback system 1902 detectsthe end or probable end of an event of interest, it may send an output2704 or an output signal to the measurement sensor unit 1904 and/or themeasurement sensor processing unit 1909. Upon receiving the output 2704,or some time thereafter, the measurement sensor unit 1904 and/or themeasurement sensor processing unit 1909 may initiate a state change.Such a state change may include any of the following, withoutlimitation: (1) deactivating one or more circuits or components; (2)placing one or more circuits or components in a lower performance mode;(3) placing one or more circuits or components in a lower power mode;(4) reducing the power supplied to one or more circuits or components;(5) reducing the sampling rate of one or more circuits or components;(6) increasing the latency of a communication channel such as reducing asensor read out frequency; (7) reducing a buffer space allocated fordata storage. Other actions are also possible. In this way, the systemmay conserve battery life and/or processing capacity.

For consistency with FIG. 19 and FIG. 21 , the system 2700 has beendescribed with reference to the dietary tracking and feedback system1902. In practice, however, any type of physical behavior can be trackedand detected in the context of the system 2700. Accordingly, themeasurement sensor unit 1904 and/or the measurement sensor processingunit can be adjusted in an ongoing manner based on the detection ofphysical behavior events that need not be explicitly linked to eating,drinking, meal consumption, etc.

FIG. 28 is a flow chart that illustrates an exemplary embodiment of agesture-based physical behavior detection process 2800, and FIG. 29 is aflow chart that illustrates an exemplary embodiment of a process 2900for controlling operation of a medication dispensing system in a mannerthat is influenced by detected gestures.

The physical behavior detection process 2800 relates to the manner inwhich the dietary tracking and feedback system 1902 operates to detectuser gestures and associated gesture-based physical behavior events,which in turn can be used by the dietary prediction system 2104 in anongoing manner. Accordingly, this description of the process 2800 refersto historical or previously-detected events that can be leveraged by thesystem while detecting ongoing events in a real-time manner.

The illustrated embodiment of the process 2800 obtains sensor readingsfrom one or more gesture sensors (task 2802), e.g., the sensor units1900 shown in FIG. 21 or sensors used by the sensor units 1900. Asexplained above, the sensor readings indicate, measure, or identifymovement, motion, or positional change of at least one specific bodypart of the user. Optionally, the process 2800 may obtain ancillaryinput from at least one ancillary input component, such as thecomponents, units, or sensors associated with the ancillary eventdetection system 2102 (task 2804). In certain embodiments, the ancillaryevent detection system 2102 and its associated ancillary inputcomponents are distinct from the gesture sensors. In this regard, thegesture sensors may be realized as user-worn or user-carried sensordevices having accelerometer and/or gyroscope elements, and theancillary input components may be realized as physically distinct andseparate devices that are designed to sense or detect other conditions,phenomena, user status, and/or environmental states that are notdirectly related to body movement or user gestures. For the dietarytracking embodiment described here, the gesture sensors are configuredand operated to obtain sensor data used to detect physical gestures thatare directly related to (or directly indicate) eating or drinkingmovements, such as bites, sips, chewing, motion corresponding to the useof eating utensils, etc. For such an embodiment, the ancillary input mayinclude any type or amount of information that might be correlated to oronly indirectly associated with an eating event, a drinking event, or afood intake event.

The process 2800 continues by analyzing the received sensor readings andthe received ancillary input (if applicable) to detect or declareoccurrences of certain gesture-based physical behavior events (task2806). In practice, the analysis performed at task 2806 primarily relieson the information obtained from the gesture sensors, as described indetail above. Nonetheless, ancillary input can also be leveraged tobetter inform the decision made at task 2806, to improve the reliabilityof the determination, and the like. This description assumes that theprocess 2800 detects a number of gesture-based physical behavior eventsover time, and assumes that information and/or metadata associated withdetected events is saved in a suitably configured data storage device.More specifically, the process 2800 stores historical event informationcorresponding to the detected occurrences of the gesture-based physicalbehavior events (task 2808). The stored event information may includeany of the following, without limitation: the type of gesture detected(e.g., bite, sip, inhale, knife cut gesture, utensil usage gesture); thenumber of times each type of gesture was detected during a period oftime (e.g., the last 24 hours, the last hour, the last 15 minutes or anyother period of time); a probability or confidence measure for thedetected gesture; the duration of the gesture; time/date stamp data;ancillary data associated with the ancillary input; the type of eventdetected (e.g., eating a meal, drinking coffee, eating a snack,cooking); a probability or confidence measure for the detected event;the duration of the event. Over time, a rich database of historicalevent information can be maintained to provide a good foundation for theprediction system.

Referring to FIG. 29 , the following description of the system controlprocess 2900 assumes that at least a baseline amount of historical eventinformation has already been saved for use with ongoing processing ofreal-time gesture sensor measurements. The illustrated embodiment of theprocess 2900 receives current sensor readings obtained from the gesturesensors in real-time (task 2902). The process 2900 may also receive atleast one ancillary input 2106 from the detection system 2102, which isdistinct from the gesture sensors (task 2904). The gesture sensors andcorresponding sensor readings, and the detection system 2102 andcorresponding ancillary input 2106 were described in detail above. Thecurrent gesture sensor readings and ancillary input (if applicable) canbe reviewed with historical event information to carry out theprediction techniques described herein. Accordingly, the process 2900retrieves or accesses at least some of the stored historical eventinformation (task 2906).

The stored historical event information is processed at task 2908 topredict a future occurrence of a physical behavior event of interest.Although not always required, the processing at task 2908 may alsoconsider the current gesture sensor readings and/or the currentancillary input when generating the prediction. Task 2908 analyzes thehistorical event information to predict whether a particular physicalbehavior event is likely to occur in the future. At least some of thecurrent sensor readings can be compared to stored historical eventinformation during this analysis. Various prediction methodologies andexamples were described above with reference to FIG. 21 and, morespecifically, with reference to the dietary prediction system 2104. Theprocess 2900 can leverage any of the previously described techniques andmethodologies to predict future occurrences of physical behavior events.

In certain embodiments, task 2908 may also consider the current gesturesensor readings, which might increase the accuracy of the prediction.Accordingly, task 2908 may predict the future occurrence of the eventbased on certain characteristics of a currently detected gesture-basedphysical behavior event. Task 2908 may also consider current ancillaryinput(s) to increase the accuracy of the prediction. In this regard, theprocess 2900 may calculate a probability or confidence level for thepredicted future occurrence of the event (task 2910). For example, theprocess 2900 may declare that a predicted food intake event will occurwithin the next 15 minutes, with a corresponding probability orconfidence level, such as 83% probability. The probability or confidencelevel may be calculated from a review of the historical eventinformation, with or without additional sensor data or statusinformation collected in real-time.

In certain embodiments, the process 2900 generates and sends a messageto the user—the message conveys information or content that is relatedto the predicted future occurrence of the event of interest (task 2912).For example, task 2912 may initiate delivery or display of anotification, email, or text message to the user, where the deliveredcontent identifies the predicted event, a predicted time of the event,the probability or confidence level, and possibly a recommendation oradvice regarding actions that the user should take (e.g., medicationdosage, carbohydrate consumption, or the like).

The process 2900 may also generate and provide predictive information toat least one component, functional module, or device of the system (task2914). For this example, the dietary prediction system 2104 of FIG. 21provides predictive information 2116 to the medication dosingcalculation unit 1906 and, optionally, to the dietary tracking andfeedback system 1902. In this context, the predictive informationprovided at task 2914 may include any of the following, withoutlimitation: the predicted event (e.g., food intake, eating dinner,drinking a beverage, consuming a snack, performing a physical activitythat is being monitored); the probability or confidence measure for thepredicted event; and a predicted start time, end time, and/or durationof the predicted event. The predictive information provided at task 2914can be used to adjust medication dosage, medication dispensingparameters, or both (task 2916). In this regard, the predicted futureoccurrence of the physical behavior event of interest and its calculatedconfidence level can be processed to influence the medication dosageand/or medication dispensing parameters. In accordance with the insulindelivery implementation described herein, the predicted event is a foodintake event, the medication managed by the system is insulin, and task2916 calculates an appropriate dosage of insulin to be administeredunder the assumption that the predicted food intake event will actuallyoccur. As another example, the message sent at task 2912 may inform theuser that the system has predicted an upcoming food intake event, andrecommend an appropriate bolus dose of insulin for the predicted foodintake event.

FIG. 30 is a flow chart that illustrates an exemplary embodiment of aprocess 3000 for controlling operation of a medication dispensingsystem, which includes the adjustment of a confidence level for adetected behavior event. The process 3000 may be implemented as a partof, or performed in association with, one or more of the other systemcontrol processes described elsewhere herein. Indeed, one or more of thetasks shown in FIG. 30 may be similar or equivalent to counterpart tasksdescribed with reference to FIG. 28 or FIG. 29 . For the sake of brevityand clarity, similar or equivalent tasks or aspects of the process 3000will not be redundantly described in detail here.

The illustrated embodiment of the process 3000 obtains gesture sensorreadings from one or more gesture sensors of the system (task 3002), asdescribed above with reference to task 2802 of FIG. 28 and in othercontexts. The sensor readings are analyzed and processed to detect anoccurrence of a gesture-based physical behavior event (task 3004). Incertain embodiments, the analysis and processing performed at task 3004also considers supplemental information (data generated by sources otherthan the gesture sensors), as described above with reference to task2806 of FIG. 28 and in other contexts.

The process 3000 calculates an initial confidence level for the detectedoccurrence of the gesture-based physical behavior event (task 3006), asdescribed above with reference to task 2910 of FIG. 29 and in othercontexts. The process 3000 continues by obtaining external informationfrom at least one source of data this is distinct from the gesturesensors (task 3008). In this regard, the external information can besupplemental or ancillary data other than the gesture sensor data. Forexample, the at least one source of data may include the ancillarydetection system 2102 (see FIG. 21 and the related description), whichis physically distinct from the gesture sensors. As explained above, theancillary detection system 2102 may include or cooperate with at leastone component configured to generate user status information that is notdirectly related to user gestures or the detection of user gestures. Theexternal information processed at task 3008 may include any amount ofthe user status information provided by the component(s) of theancillary detection system 2102. Accordingly, the user statusinformation may include any of the various types of ancillary input 2106mentioned above with reference to FIG. 21 .

As another example, the at least one source of data may include ameasurement sensor unit 1904 (see FIG. 21 and the related description)that generates sensor data indicative of a physiological characteristicof the user. Alternatively or additionally, the at least one source ofdata may include the measurement sensor processing unit 1909 thatcooperates with the measurement sensor unit 1904. For this example,therefore, the external information processed at task 3008 may includethe sensor data generated by the measurement sensor unit 1904 and/or themeasurement sensor processing unit 1909.

As another example, the at least one source of data may include aprediction system configured to predict future occurrences ofgesture-based physical behavior events, wherein the external informationincludes predictive information generated by the prediction system. Inthis context, the predictive information describes, characterizes, ordefines a predicted future occurrence of a gesture-based physicalbehavior event. In certain embodiments, the detected occurrence of thegesture-based physical behavior event corresponds to a food intakeevent, and the prediction system is implemented as the dietaryprediction system 2104 (see FIG. 21 and the associated description).

Any or all of the available external information can be used to modify,adjust, or verify the initial confidence level calculated at task 3006.More specifically, the process 3000 may continue by deriving a finalconfidence level for the detected occurrence of the gesture-basedphysical behavior event (task 3010). The final confidence level isderived from the initial confidence level and from at least some of theexternal information obtained from the source(s) of external data. Thus,if the external information further indicates that the gesture-basedphysical behavior event of interest has occurred or is likely to occur,then task 3008 can increase the initial confidence level. Conversely, ifthe external information does not support or agree with thedetermination made at task 3004, then task 3008 can decrease the initialconfidence level. If the external information is “neutral” or isotherwise insufficient to influence the initial confidence level, thentask 3008 can preserve the initial confidence level.

The process 3000 may continue by adjusting the medication dosage and/orthe medication dispensing parameters in response to the detectedoccurrence of the gesture-based physical behavior event and in responseto the final confidence level (task 3012). Task 3012 is similar to task2916 described above with reference to FIG. 29 . For a detected foodintake event, task 3012 can calculate the dosage of insulin to bedelivered to the user, along with certain insulin dispensing parameters.The final confidence level may be used to influence the aggressivenessof the medication treatment, the dosage, or the like. For example, ifthe final confidence level is very high, then a first dosage of insulinmay be calculated for the detected food intake event. If, however, thefinal confidence level is below a confidence threshold (e.g., only about80%), then a second (lower) dosage of insulin may be calculated as asafe measure. The rate of insulin delivery may also be adjustable basedon the final confidence level. These and other examples are contemplatedby the process 3000.

FIG. 31 is a flow chart that illustrates an exemplary embodiment of aprocess 3100 for controlling operation of a medication dispensingsystem, which includes the adjustment of criteria used to determinewhether a gesture-based behavior event has occurred. The process 3100may be implemented as a part of, or performed in association with, oneor more of the other system control processes described elsewhereherein. Indeed, one or more of the tasks shown in FIG. 31 may be similaror equivalent to counterpart tasks described with reference to FIG. 28 ,FIG. 29 , or FIG. 30 . For the sake of brevity and clarity, similar orequivalent tasks or aspects of the process 3100 will not be redundantlydescribed in detail here.

The illustrated embodiment of the process 3100 maintains default eventdeclaration criteria for use in determining whether a gesture-basedphysical behavior event of interest has occurred or is likely to occur(task 3102). The default criteria can be maintained in a suitablyconfigured and formatted memory or data storage element of the system.Examples of event declaration criteria were described above withreference to FIGS. 23-26 . Event declaration criteria may include, forexample: the type of gesture(s) detected; the number of each gesturedetected per unit of time; probability or confidence levels associatedwith detected gestures; the presence of designated sequences or patternsof gestures; and the like. In this regard, default event declarationcriteria can be maintained for any number of gesture-based physicalbehavior events that the system intends to monitor.

The process 3100 obtains gesture sensor readings from one or moregesture sensors of the system (task 3104), as described above withreference to task 2802 of FIG. 28 and in other contexts. The process3100 also obtains external information from at least one source of datathis is distinct from the gesture sensors (task 3106). The externalinformation and possible sources of external information were describedabove with reference to task 3008 of the process 3000, and thatdescription also applies here in the context of process 3100.Accordingly, the at least one source of data may include, withoutlimitation: the ancillary detection system 2102; the measurement sensorunit(s) 1904; the measurement sensor processing unit 1909; and/or aprediction system such as the dietary prediction system 2104.

Any or all of the available external information can be used to modify,adjust, or preserve the default event declaration criteria. Morespecifically, the process 3100 may continue by deriving final eventdeclaration criteria for use in determining whether the gesture-basedphysical behavior event of interest has occurred or is likely to occur(task 3108). The final event declaration criteria are derived from thedefault event declaration criteria and from at least some of theexternal information obtained from the source(s) of external data. Thus,if the external information indicates with high confidence that thegesture-based physical behavior event of interest has occurred or islikely to occur, then task 3108 can decrease or relax the requirementsof the default event declaration criteria. Conversely, if the externalinformation indicates with low confidence that the event of interest hasoccurred or is likely to occur, or that the event of interest has not orwill not occur, then task 3108 can increase or tighten the requirementsof the default event declaration criteria for that event. If theexternal information is “neutral” or is otherwise insufficient toinfluence the default event declaration criteria, then task 3108 canpreserve the default criteria.

The gesture sensor readings are analyzed and processed to detect anoccurrence of the gesture-based physical behavior event of interest(task 3110). In certain embodiments, the analysis and processingperformed at task 3110 also considers supplemental information (datagenerated by sources other than the gesture sensors), as described abovewith reference to task 2806 of FIG. 28 and in other contexts. The systemapplies and considers the final event declaration criteria to determineswhether the gesture-based physical behavior event of interest hasoccurred or is likely to occur (query task 3112). When the obtainedgesture sensor readings satisfy the final event declaration criteria(the “Yes” branch of query task 3112), the process 3100 declares adetection of that particular gesture-based physical behavior event (task3114). If the criteria is not satisfied (the “No” branch of query task3112), then the process 3100 may exit or return to task 3104 forcontinued monitoring and processing of gesture sensor readings.

The process 3100 may continue by adjusting the medication dosage and/orthe medication dispensing parameters in response to the declaredoccurrence of the gesture-based physical behavior event (task 3116).Task 3116 is similar to task 2916 described above with reference to FIG.29 . For a detected food intake event, task 3116 can calculate thedosage of insulin to be delivered to the user, along with certaininsulin dispensing parameters.

FIG. 32 is a flow chart that illustrates an exemplary embodiment of aprocess 3200 for controlling operation of a medication dispensingsystem, which includes the adjustment of at least one sensor systemoperating parameter in response to the detection of a gesture-basedbehavior event. The process 3200 may be implemented as a part of, orperformed in association with, one or more of the other system controlprocesses described elsewhere herein. Indeed, one or more of the tasksshown in FIG. 32 may be similar or equivalent to counterpart tasksdescribed with reference to FIGS. 28-31 . For the sake of brevity andclarity, similar or equivalent tasks or aspects of the process 3200 willnot be redundantly described in detail here.

The illustrated embodiment of the process 3200 obtains gesture sensorreadings from one or more gesture sensors of the system (task 3202), asdescribed above with reference to task 2802 of FIG. 28 and in othercontexts. The sensor readings are analyzed and processed to detect anoccurrence of a gesture-based physical behavior event (task 3204). Incertain embodiments, the analysis and processing performed at task 3204also considers supplemental information (data generated by sources otherthan the gesture sensors), as described above with reference to task2806 of FIG. 28 and in other contexts.

In response to the detection of a gesture-based physical behavior event,the process 3200 generates and provides corresponding event informationto a component of the sensor system (task 3206), wherein the sensorsystem includes, for example, the measurement sensor 2701 and/or themeasurement sensor processor 2702 shown in FIG. 27 . The eventinformation includes data that describes, characterizes, or defines thedetected occurrence of the gesture-based physical behavior event, suchas a food intake event. For a food intake event, the event informationmay include, for example, any of the information specified above withreference to task 2808 of the process 2800 (see FIG. 28 and the relateddescription). The event information can be arranged, formatted, andexpressed in an appropriate manner for purposes of controlling theadjustment of operating parameter(s) of the sensor system, to initiatethe adjustment or regulation of such operating parameter(s), or thelike. In practice, therefore, the event information is formatted andcommunicated in a way that is compatible with the sensor system. Incertain embodiments, the event information can also be arranged,formatted, and expressed in an appropriate manner for purposes ofcontrolling the adjustment of medication dosage and/or medicationdispensing parameters of the system, as explained in more detail above.

This description assumes that at least one component of the sensorsystem receives and processes the event information (task 3208). Morespecifically, the received event information is processed to initiatethe adjustment of one or more operating parameters of the sensor system.Thus, at least one operating parameter of the measurement sensor 2701and/or at least one operating parameter of the measurement sensorprocessor 2702 can be adjusted in accordance with the provided eventinformation. In this regard, task 3208 may be associated with one ormore of the following, without limitation: changing an amount of powersupplied to the measurement sensor and/or the measurement sensorprocessor; altering a measurement sampling rate of the measurementsensor; modifying the timing of output generated by the measurementsensor processor; varying a performance mode of the measurement sensorand/or the measurement sensor processor. Other adjustments can also bemade, as appropriate for the particular embodiment (see, for example,FIG. 27 and the foregoing description of the system 2700).

As mentioned above, the measurement sensor 2701 generates sensor datathat indicates the measured physiological characteristic of the user,such as blood glucose. The measurement sensor processor 2702 generatescorresponding sensor measurement information from the sensor data (task3210). The sensor data and/or the sensor measurement information isgenerated, processed, and/or communicated in a manner that is influencedby the provided event information (because the event informationinitiates the adjustment of one or more operating parameters of thesensor system).

The process 3200 may continue by controlling, adjusting, or regulatingthe medication dosage and/or the medication dispensing parameters inresponse to the sensor measurement information (task 3212). In certainembodiments, task 3212 controls, adjusts, or regulates the medicationdosage and/or the medication dispensing parameters in response to thedetected occurrence of the gesture-based physical behavior event, asindicated by the event information. For a detected food intake event,task 3212 can calculate the dosage of insulin to be delivered to theuser and manage the dispensing of the insulin dose. The medicationdosage and/or the medication dispensing parameters can be controlled,adjusted, or regulated using any of the techniques or methodologiesdescribed previously. For example, the medication dosage and/ordispensing parameters can be adjusted based on the current sensorvalues, predicted levels of the measured characteristic, the type ofgesture(s) detected, confidence levels associated with detectedgesture(s), bite count, sip count, current or historical ancillary(external) information, or the like.

EXAMPLES

Example 1: An automated medication dosing and dispensing systemcomprising: sensors to detect movement and other physical inputs relatedto a user of the automated medication dosing and dispensing system; acomputer-readable storage medium comprising program code instructions;and a processor, wherein the program code instructions are configurableto cause the processor to perform a method comprising the steps of:determining, from sensor readings obtained from the sensors, occurrenceof a gesture-based physical behavior event of the user; and adjustingmedication dosage, medication dispensing parameters, or both medicationdosage and medication dispensing parameters in response to thedetermining.

Example 2: The system of Example 1, wherein at least one of the sensorreadings measures a movement of a body part of the user.

Example 3: The system of Example 1, further comprising an eventdetection module to determine, from the sensor readings, gestures of theuser.

Example 4: The system of Example 1, wherein the method further comprisesthe step of sending a message to the user, wherein the message relatesto the adjusting.

Example 5: The system of Example 1, wherein the gesture-based physicalbehavior event corresponds to user activity that is unrelated to a foodintake event.

Example 6: The system of Example 5, wherein the user activity that isunrelated to the food intake event comprises a smoking event, a personalhygiene event, and/or a medication related event.

Example 7: The system of Example 1, wherein the gesture-based physicalbehavior event corresponds to a food intake event.

Example 8: The system of Example 1, wherein the adjusting is performedupon detection of an actual, probable, or imminent start of thegesture-based physical behavior event.

Example 9: The system of Example 1, wherein the adjusting is based oncharacteristics of the gesture-based physical behavior event.

Example 10: The system of Example 9, wherein: the gesture-based physicalbehavior event corresponds to a food intake event; and the adjusting isbased on at least one of the following characteristics of the foodintake event: time duration; pace; start time; end time; number ofbites; number of sips; eating method; type of utensils used; type ofcontainers used; amount of chewing before swallowing; chewing speed;amount of food consumed; amount of carbohydrates consumed, time betweenbites; time between sips; content of food consumed.

Example 11: The system of Example 1, wherein: the medication managed bythe system is insulin; and the adjusting step calculates a dosage ofinsulin to be administered and a schedule for delivery of the calculateddosage of insulin.

Example 12: The system of Example 1, wherein the sensors comprise anaccelerometer that measures movement of an arm of the user and agyroscope that measures rotation of the arm of the user.

Example 13: A method of operating an automated medication dosing anddispensing system having sensors to detect movement and other physicalinputs related to a user, the method comprising the steps of: obtaining,using a processor of the automated medication dosing and dispensingsystem, a set of sensor readings, wherein at least one sensor reading ofthe set of sensor readings measures a movement of a body part of a user;determining, from the set of sensor readings, occurrence of agesture-based physical behavior event of the user; and adjustingmedication dosage, medication dispensing parameters, or both medicationdosage and medication dispensing parameters in response to thedetermining.

Example 14: The method of Example 13, further comprising the step ofperforming a computer-based action in response to the determining,wherein the computer-based action is one or more of: obtaining otherinformation to be stored in memory in association with data representingthe gesture-based physical behavior event; interacting with the user toprovide information or a reminder; interacting with the user to promptfor user input; sending a message to a remote computer system; sending amessage to another person; sending a message to the user.

Example 15: The method of Example 13, wherein the gesture-based physicalbehavior event corresponds to user activity that is unrelated to a foodintake event.

Example 16: The method of Example 15, wherein the user activity that isunrelated to the food intake event comprises a smoking event, a personalhygiene event, and/or a medication related event.

Example 17: The method of Example 13, wherein the gesture-based physicalbehavior event corresponds to a food intake event.

Example 18: The method of Example 13, wherein the adjusting is performedupon detection of an actual, probable, or imminent start of thegesture-based physical behavior event.

Example 19: The method of Example 13, wherein the adjusting is based oncharacteristics of the gesture-based physical behavior event.

Example 20: The method of Example 19, wherein: the gesture-basedphysical behavior event corresponds to a food intake event; and theadjusting is based on at least one of the following characteristics ofthe food intake event: time duration; pace; start time; end time; numberof bites; number of sips; eating method; type of utensils used; type ofcontainers used; amount of chewing before swallowing; chewing speed;amount of food consumed; time between bites; time between sips; contentof food consumed.

Example 21: An automated medication dosing and dispensing systemcomprising: sensors to detect movement related to a user of theautomated medication dosing and dispensing system; a computer-readablestorage medium comprising program code instructions; and a processor,wherein the program code instructions are configurable to cause theprocessor to perform a method comprising the steps of: determining, fromsensor readings obtained from the sensors, a start or an anticipatedstart of a current food intake event of the user; reviewing historicaldata collected for previously recorded food intake events of the user;identifying a correlation between the current food intake event and anumber of the previously recorded food intake events; and adjustingmedication dosage, medication dispensing parameters, or both medicationdosage and medication dispensing parameters based on the identifiedcorrelation.

Example 22: The system of Example 21, wherein at least one of the sensorreadings measures a movement of a body part of the user.

Example 23: The system of Example 21, further comprising an eventdetection module to determine, from the sensor readings, a physicalbehavior event of the user.

Example 24: The system of Example 23, wherein the event detection moduledetermines gestures of the user that characterize the current foodintake event.

Example 25: The system of Example 21, wherein the adjusting is based onat least one of the following characteristics of the food intake event:time duration; pace; start time; end time; number of bites; number ofsips; eating method; type of utensils used; type of containers used;amount of chewing before swallowing; chewing speed; amount of foodconsumed; time between bites; time between sips; content of foodconsumed.

Example 26: The system of Example 21, wherein: the medication managed bythe system is insulin; and the adjusting step calculates a dosage ofinsulin to be administered and a schedule for delivery of the calculateddosage of insulin.

Example 27: The system of Example 21, wherein the sensors comprise anaccelerometer that measures movement of an arm of the user and agyroscope that measures rotation of the arm of the user.

Example 28: The system of Example 21, wherein the historical datacomprises parameters that are not directly linked to the food intakeevent.

Example 29: The system of Example 28, wherein the parameters include atleast one of: location information; time of day the user wakes up;stress level; sleeping behavior patterns; calendar event details; phonecall information; email meta-data.

Example 30: A method of operating an automated medication dosing anddispensing system having sensors to detect movement related to a user,the method comprising the steps of: determining, from sensor readingsobtained from the sensors, a start or an anticipated start of a currentfood intake event of the user; reviewing historical data collected forpreviously recorded food intake events of the user; identifying acorrelation between the current food intake event and a number of thepreviously recorded food intake events; and adjusting medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters based on the identified correlation.

Example 31: The method of Example 30, wherein at least one of the sensorreadings measures a movement of a body part of the user.

Example 32: The method of Example 30, further comprising the step ofdetermining, from the sensor readings, physical behavior events of theuser.

Example 33: The method of Example 32, wherein the physical behaviorevents determined from the sensor readings include gestures of the userthat characterize the current food intake event.

Example 34: The method of Example 30, wherein the adjusting is based onat least one of the following characteristics of the food intake event:time duration; pace; start time; end time; number of bites; number ofsips; eating method; type of utensils used; type of containers used;amount of chewing before swallowing; chewing speed; amount of foodconsumed; time between bites; time between sips; content of foodconsumed.

Example 35: The method of Example 30, wherein: the medication managed bythe system is insulin; and the adjusting step calculates a dosage ofinsulin to be administered and a schedule for delivery of the calculateddosage of insulin.

Example 36: The method of Example 30, wherein the sensors comprise anaccelerometer that measures movement of an arm of the user and agyroscope that measures rotation of the arm of the user.

Example 37: The method of Example 30 wherein the historical datacomprises parameters that are not directly linked to the food intakeevent.

Example 38: The method of Example 37, wherein the parameters include atleast one of: location information; time of day the user wakes up;stress level; sleeping behavior patterns; calendar event details; phonecall information; email meta-data.

Example 39: The method of Example 30, wherein the adjusting is performedupon detection of an actual or imminent start of the current food intakeevent.

Example 40: The method of Example 30, wherein the adjusting is based oncharacteristics of the current gesture-based physical behavior event.

Example 41: An automated medication dosing and dispensing systemcomprising: sensors to detect physical movement of a user of theautomated medication dosing and dispensing system; computer-readablestorage media comprising program code instructions; and at least oneprocessor, wherein the program code instructions are configurable tocause the at least one device to perform a method comprising the stepsof: detecting, based on analysis of sensor readings obtained from thesensors, occurrences of gesture-based physical behavior events; storinghistorical event information corresponding to the detected occurrencesof gesture-based physical behavior events; processing the storedhistorical event information to predict a future occurrence of aphysical behavior event of interest; and adjusting medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the predicted futureoccurrence of the physical behavior event of interest.

Example 42: The system of Example 41, wherein the processing stepcomprises processing the stored historical event information and atleast one ancillary input received from a detection system that isdistinct from the sensors.

Example 43: The system of Example 41, wherein the processing stepcomprises processing the stored historical event information and currentsensor readings obtained from the sensors in real-time, includingcomparing the current sensor readings to the stored historical eventinformation.

Example 44: The system of Example 41, wherein at least one of the sensorreadings measures a movement of a specific body part of the user.

Example 45: The system of Example 41, wherein the method performed bythe at least one processor further comprises the step of sending amessage to the user, wherein the message relates to the predicted futureoccurrence of the physical behavior event of interest.

Example 46: The system of Example 41, wherein: the detected occurrencesof gesture-based physical behavior events correspond to food intakeevents; and the adjusting is performed in response to a predicted foodintake event.

Example 47: The system of Example 41, wherein: the medication managed bythe system is insulin; and the adjusting step calculates a dosage ofinsulin to be administered.

Example 48: The system of Example 41, wherein: the method performed bythe at least one processor further comprises the step of calculating aconfidence level for the predicted future occurrence of the physicalbehavior event of interest; and the adjusting is performed in responseto the confidence level.

Example 49: A method of operating an automated medication dosing anddispensing system having sensors that detect movement of a user, themethod comprising the steps of: obtaining sensor readings from thesensors; detecting, based on analysis of the obtained sensor readings,occurrences of gesture-based physical behavior events; storinghistorical event information corresponding to the detected occurrencesof gesture-based physical behavior events; processing, with at least oneprocessor of the automated medication dosing and dispensing system, thestored historical event information to predict a future occurrence of aphysical behavior event of interest; and adjusting medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the predicted futureoccurrence of the physical behavior event of interest.

Example 50: The method of Example 49, wherein the processing stepcomprises processing the stored historical event information and atleast one ancillary input received from a detection system that isdistinct from the sensors.

Example 51: The method of Example 50, wherein: the detected occurrencesof gesture-based physical behavior events correspond to food intakeevents; and the at least one ancillary input comprises information thatis not directly related to food intake events.

Example 52: The method of Example 49, wherein at least one of the sensorreadings measures a movement of a specific body part of the user.

Example 53: The method of Example 49, further comprising the step ofsending a message to the user, wherein the message relates to thepredicted future occurrence of the physical behavior event of interest.

Example 54: The method of claim 49, wherein: the detected occurrences ofgesture-based physical behavior events correspond to food intake events;and the adjusting is performed in response to a predicted food intakeevent.

Example 55: The method of Example 49, wherein: the medication managed bythe system is insulin; and the adjusting step calculates a dosage ofinsulin to be administered.

Example 56: The method of Example 49, further comprising the step ofcalculating a confidence level for the predicted future occurrence ofthe physical behavior event of interest, wherein the adjusting isperformed in response to the confidence level.

Example 57: A method of operating an automated medication dosing anddispensing system having sensors that detect movement, the methodcomprising the steps of: obtaining sensor readings from the sensors, thesensor readings indicating movement of at least one body part of theuser; detecting, based on analysis of the obtained sensor readings,occurrences of gesture-based physical behavior events; storinghistorical event information corresponding to the detected occurrencesof gesture-based physical behavior events; receiving at least oneancillary input from a detection system that is distinct from thesensors; processing, with at least one processor of the automatedmedication dosing and dispensing system, the stored historical eventinformation and the at least one ancillary input to predict a futureoccurrence of a physical behavior event of interest; and adjustingmedication dosage, medication dispensing parameters, or both medicationdosage and medication dispensing parameters in response to the predictedfuture occurrence of the physical behavior event of interest.

Example 58: The method of Example 57, wherein: the detected occurrencesof gesture-based physical behavior events correspond to food intakeevents; the at least one ancillary input comprises information that isnot directly related to food intake events; and the adjusting isperformed in response to a predicted food intake event.

Example 59: The method of Example 57, further comprising the step ofcalculating a confidence level for the predicted future occurrence ofthe physical behavior event of interest, wherein the adjusting isperformed in response to the confidence level.

Example 60: The method of Example 57, wherein the processing steppredicts the future occurrence of the physical behavior event ofinterest based on characteristics of a currently detected gesture-basedphysical behavior event.

Example 61: An automated medication dosing and dispensing systemcomprising: gesture sensors to detect physical movement of a user of theautomated medication dosing and dispensing system; computer-readablestorage media comprising program code instructions; and at least oneprocessor, wherein the program code instructions are configurable tocause the at least one processor to perform a method comprising thesteps of: detecting, based on analysis of gesture sensor readingsobtained from the gesture sensors, an occurrence of a gesture-basedphysical behavior event; calculating an initial confidence level for thedetected occurrence of the gesture-based physical behavior event;deriving a final confidence level, for the detected occurrence of thegesture-based physical behavior event, from the initial confidence leveland from external information obtained from at least one source of datathat is distinct from the gesture sensors; and adjusting medicationdosage, medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the detected occurrenceof the gesture-based physical behavior event and the final confidencelevel.

Example 62: The system of Example 61, wherein: the at least one sourceof data comprises an ancillary event detection system that is physicallydistinct from the gesture sensors; the ancillary event detection systemcomprises at least one component to generate user status informationthat is not directly related to user gestures; and the externalinformation comprises the user status information.

Example 63: The system of Example 61, wherein: the at least one sourceof data comprises a measurement sensor that generates sensor dataindicative of a physiological characteristic of the user; the externalinformation comprises the sensor data; and the medication dosage or themedication dispensing parameters are determined based on the sensordata.

Example 64: The system of Example 63, wherein the measurement sensorcomprises a continuous glucose monitor.

Example 65: The system of Example 61, wherein: the at least one sourceof data comprises a prediction system to predict future occurrences ofgesture-based physical behavior events; and the external informationcomprises predictive information generated by the prediction system, thepredictive information describing, characterizing, or defining apredicted future occurrence of a gesture-based physical behavior event.

Example 66: The system of Example 61, wherein: the detected occurrenceof the gesture-based physical behavior event corresponds to a foodintake event; the medication managed by the system is insulin; and theadjusting step calculates a dosage of insulin to be administered.

Example 67: A method of operating an automated medication dosing anddispensing system having gesture sensors that detect physical movementof a user, the method comprising the steps of: obtaining gesture sensorreadings from the gesture sensors; detecting, based on analysis of theobtained gesture sensor readings, an occurrence of a gesture-basedphysical behavior event; calculating an initial confidence level for thedetected occurrence of the gesture-based physical behavior event;deriving a final confidence level, for the detected occurrence of thegesture-based physical behavior event, from the initial confidence leveland external information obtained from at least one source of data thatis distinct from the gesture sensors; and adjusting medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the detected occurrenceof the gesture-based physical behavior event and the final confidencelevel.

Example 68: The method of Example 67, wherein: the at least one sourceof data comprises an ancillary event detection system that is physicallydistinct from the gesture sensors; the ancillary event detection systemcomprises at least one component to generate user status informationthat is not directly related to user gestures; and the externalinformation comprises the user status information.

Example 69: The method of Example 67, wherein: the at least one sourceof data comprises a measurement sensor that generates sensor dataindicative of a physiological characteristic of the user; and theexternal information comprises the sensor data.

Example 70: The method of Example 67, wherein: the at least one sourceof data comprises a prediction system to predict future occurrences ofgesture-based physical behavior events; and the external informationcomprises predictive information generated by the prediction system, thepredictive information describing, characterizing, or defining apredicted future occurrence of a gesture-based physical behavior event.

Example 71: An automated medication dosing and dispensing systemcomprising: gesture sensors to detect physical movement of a user of theautomated medication dosing and dispensing system; computer-readablestorage media comprising program code instructions; and at least oneprocessor, wherein the program code instructions are configurable tocause the at least one processor to perform a method comprising thesteps of: maintaining default event declaration criteria for use indetermining whether a gesture-based physical behavior event of interesthas occurred or is likely to occur; obtaining gesture sensor readingsfrom the gesture sensors; deriving final event declaration criteria, foruse in determining whether the gesture-based physical behavior event ofinterest has occurred or is likely to occur, from the default eventdeclaration criteria and from external information obtained from atleast one source of data that is distinct from the gesture sensors;analyzing the obtained gesture sensor readings to determine whether thefinal event declaration criteria is satisfied; when the obtained gesturesensor readings satisfy the final event declaration criteria, declaringa detection of the gesture-based physical behavior event of interest;and adjusting medication dosage, medication dispensing parameters, orboth medication dosage and medication dispensing parameters in responseto the declaring.

Example 72: The system of Example 71, wherein the deriving stepdecreases requirements of the default event declaration criteria whenthe external information indicates with high confidence that thegesture-based physical behavior event of interest has occurred,increases requirements of the default event declaration criteria whenthe external information indicates with low confidence that thegesture-based physical behavior event of interest has occurred, orpreserves the default event declaration criteria when the externalinformation is insufficient to influence the default event declarationcriteria.

Example 73: The system of Example 71, wherein: the at least one sourceof data comprises an ancillary event detection system that is physicallydistinct from the gesture sensors; the ancillary event detection systemcomprises at least one component to generate user status informationthat is not directly related to user gestures; and the externalinformation comprises the user status information.

Example 74: The system of Example 71, wherein: the at least one sourceof data comprises a measurement sensor that generates sensor dataindicative of a physiological characteristic of the user; and theexternal information comprises the sensor data.

Example 75: The system of Example 74, wherein the measurement sensorcomprises a continuous glucose monitor.

Example 76: The system of Example 71, wherein: the at least one sourceof data comprises a prediction system to predict future occurrences ofgesture-based physical behavior events; and the external informationcomprises predictive information generated by the prediction system, thepredictive information describing, characterizing, or defining apredicted occurrence of a gesture-based physical behavior event.

Example 77: The system of Example 71, wherein: the gesture-basedphysical behavior event of interest corresponds to a food intake event;the medication managed by the system is insulin; and the adjusting stepcalculates a dosage of insulin to be administered.

Example 78: A method of operating an automated medication dosing anddispensing system having gesture sensors that detect physical movementof a user, the method comprising the steps of: maintaining default eventdeclaration criteria for use in determining whether a gesture-basedphysical behavior event of interest has occurred or is likely to occur;obtaining gesture sensor readings from the gesture sensors; derivingfinal event declaration criteria, for use in determining whether thegesture-based physical behavior event of interest has occurred or islikely to occur, from the default event declaration criteria and fromexternal information obtained from at least one source of data that isdistinct from the gesture sensors; analyzing the obtained gesture sensorreadings to determine whether the final event declaration criteria issatisfied; when the obtained gesture sensor readings satisfy the finalevent declaration criteria, declaring a detection of the gesture-basedphysical behavior event of interest; and adjusting medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the declaring.

Example 79: The method of Example 78, wherein the deriving stepdecreases requirements of the default event declaration criteria whenthe external information indicates with high confidence that thegesture-based physical behavior event of interest has occurred,increases requirements of the default event declaration criteria whenthe external information indicates with low confidence that thegesture-based physical behavior event of interest has occurred, orpreserves the default event declaration criteria when the externalinformation is insufficient to influence the default event declarationcriteria.

Example 80: The method of Example 78, wherein: the gesture-basedphysical behavior event of interest corresponds to a food intake event;the medication managed by the system is insulin; and the adjusting stepcalculates a dosage of insulin to be administered.

Example 81: An automated medication dosing and dispensing systemcomprising: gesture sensors to detect physical movement of a user of theautomated medication dosing and dispensing system; a measurement sensorto generate sensor data indicative of a physiological characteristic ofthe user; a measurement sensor processor to process the sensor datagenerated by the measurement sensor; computer-readable storage mediacomprising program code instructions; and at least one processor,wherein the program code instructions are configurable to cause the atleast one processor to perform a method comprising the steps of:detecting, based on analysis of gesture sensor readings obtained fromthe gesture sensors, an occurrence of a gesture-based physical behaviorevent; providing, to at least one of the measurement sensor and themeasurement sensor processor, event information corresponding to thedetected occurrence of the gesture-based physical behavior event; andadjusting an operating parameter of the at least one of the measurementsensor and the measurement sensor processor, in accordance with theprovided event information.

Example 82: The system of Example 81, wherein the method performed bythe at least one processor further comprises the step of: regulatingmedication dosage, medication dispensing parameters, or both medicationdosage and medication dispensing parameters in response to the detectedoccurrence of the gesture-based physical behavior event.

Example 83: The system of Example 81, wherein the method performed bythe at least one processor further comprises the steps of: generating,by the measurement sensor processor, sensor measurement information fromthe sensor data; and regulating medication dosage, medication dispensingparameters, or both medication dosage and medication dispensingparameters in response to the sensor measurement information.

Example 84: The system of Example 83, wherein the regulating stepadjusts the medication dosage, the medication dispensing parameters, orboth the medication dosage and the medication dispensing parameters inresponse to the detected occurrence of the gesture-based physicalbehavior event.

Example 85: The system of Example 83, wherein: the detected occurrenceof the gesture-based physical behavior event corresponds to a foodintake event; the system manages dosing and dispensing of insulin; andthe regulating step calculates a dosage of insulin to be administered.

Example 86: The system of Example 81, wherein the measurement sensorcomprises a continuous glucose monitor.

Example 87: The system of Example 81, wherein the provided eventinformation comprises data that describes, characterizes, or defines afood intake event.

Example 88: The system of Example 81, wherein the adjusting stepcomprises: changing an amount of power supplied to the at least one ofthe measurement sensor and the measurement sensor processor; altering ameasurement sampling rate of the measurement sensor; modifying timing ofoutput generated by the measurement sensor processor; or varying aperformance mode of at least one of the measurement sensor and themeasurement sensor processor.

Example 89: A method of operating an automated medication dosing anddispensing system comprising gesture sensors to detect physical movementof a user, a measurement sensor to generate sensor data indicative of aphysiological characteristic of the user, and a measurement sensorprocessor to process the sensor data, the method comprising the stepsof: obtaining gesture sensor readings from the gesture sensors;detecting, based on analysis of the obtained gesture sensor readings, anoccurrence of a gesture-based physical behavior event; providing, to atleast one of the measurement sensor and the measurement sensorprocessor, event information corresponding to the detected occurrence ofthe gesture-based physical behavior event, the event informationformatted to control adjustment of an operating parameter of the atleast one of the measurement sensor and the measurement sensorprocessor, in accordance with the provided event information.

Example 90: The method of Example 89, wherein the provided eventinformation is formatted to control adjustment of medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters of the system.

Example 91: The method of Example 90, wherein: the detected occurrenceof the gesture-based physical behavior event corresponds to a foodintake event; the system manages dosing and dispensing of insulin; andthe provided event information influences calculation of a dosage ofinsulin to be administered.

Example 92: The method of Example 89, wherein the provided eventinformation comprises data that describes, characterizes, or defines afood intake event.

Example 93: The method of Example 89, further comprising the step ofadjusting the operating parameter, wherein the adjusting step comprises:changing an amount of power supplied to the at least one of themeasurement sensor and the measurement sensor processor; altering ameasurement sampling rate of the measurement sensor; modifying timing ofoutput generated by the measurement sensor processor; or varying aperformance mode of at least one of the measurement sensor and themeasurement sensor processor.

Example 94: A method of operating an automated medication dosing anddispensing system comprising gesture sensors to detect physical movementof a user, a measurement sensor to generate sensor data indicative of aphysiological characteristic of the user, and a measurement sensorprocessor to process the sensor data, the method comprising the stepsof: obtaining gesture sensor readings from the gesture sensors;detecting, based on analysis of the obtained gesture sensor readings, anoccurrence of a gesture-based physical behavior event; providing, to atleast one of the measurement sensor and the measurement sensorprocessor, event information corresponding to the detected occurrence ofthe gesture-based physical behavior event; and adjusting one or moreoperating parameters of at least one of the measurement sensor and themeasurement sensor processor, in accordance with the provided eventinformation.

Example 95: The method of Example 94, further comprising the step of:regulating medication dosage, medication dispensing parameters, or bothmedication dosage and medication dispensing parameters in response tothe detected occurrence of the gesture-based physical behavior event.

Example 96: The method of Example 94, further comprising the steps of:generating, by the measurement sensor processor, sensor measurementinformation from the sensor data; and regulating medication dosage,medication dispensing parameters, or both medication dosage andmedication dispensing parameters in response to the sensor measurementinformation.

Example 97: The method of Example 96, wherein the medication dosage orthe medication dispensing parameters are determined based on the sensormeasurement information.

Example 98: The method of Example 94, wherein: the detected occurrenceof the gesture-based physical behavior event corresponds to a foodintake event; the system manages dosing and dispensing of insulin; andthe provided event information influences calculation of a dosage ofinsulin to be administered.

Example 99: The method of Example 94, wherein the provided eventinformation comprises data that describes, characterizes, or defines afood intake event.

Example 100: The method of claim 94, wherein the adjusting stepcomprises: changing an amount of power supplied to the at least one ofthe measurement sensor and the measurement sensor processor; altering ameasurement sampling rate of the measurement sensor; modifying timing ofoutput generated by the measurement sensor processor; or varying aperformance mode of at least one of the measurement sensor and themeasurement sensor processor.

CONCLUSION

As described above, there are various methods and apparatus that can beused as part of a medication dispensing regimen and alternativesprovided herein. Conjunctive language, such as phrases of the form “atleast one of A, B, and C,” or “at least one of A, B and C,” unlessspecifically stated otherwise or otherwise clearly contradicted bycontext, is otherwise understood with the context as used in general topresent that an item, term, etc., may be either A or B or C, or anynonempty subset of the set of A and B and C. For instance, in theillustrative example of a set having three members, the conjunctivephrases “at least one of A, B, and C” and “at least one of A, B and C”refer to any of the following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B,C}, {A, B, C}. Thus, such conjunctive language is not generally intendedto imply that certain embodiments require at least one of A, at leastone of B and at least one of C each to be present.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory.

The various tasks performed in connection with an illustrated processmay be performed by software, hardware, firmware, or any combinationthereof, which may be implemented with any of the devices, systems, orcomponents described herein. It should be appreciated that a describedprocess may include any number of additional or alternative tasks, thetasks shown in a flow chart need not be performed in the illustratedorder, and a described process may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown ina flow chart could be omitted from an embodiment of the describedprocess as long as the intended overall functionality remains intact.

The use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofthe invention and does not pose a limitation on the scope of theinvention unless otherwise claimed. No language in the specificationshould be construed as indicating any non-claimed element as essentialto the practice of the invention.

Further embodiments can be envisioned to one of ordinary skill in theart after reading this disclosure. In other embodiments, combinations orsub-combinations of the above-disclosed invention can be advantageouslymade. The example arrangements of components are shown for purposes ofillustration and it should be understood that combinations, additions,re-arrangements, and the like are contemplated in alternativeembodiments of the present invention. Thus, while the invention has beendescribed with respect to exemplary embodiments, one skilled in the artwill recognize that numerous modifications are possible.

For example, the processes described herein may be implemented usinghardware components, software components, and/or any combinationthereof. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims and that the invention is intended to cover allmodifications and equivalents within the scope of the following claims.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

What is claimed is:
 1. A system comprising: one or more processors; andone or more processor-readable media storing instructions which, whenexecuted by the one or more processors, cause performance of: detecting,based on processing sensor data obtained from one or more gesturesensors included in a first device, an activity in which a user of theone or more gesture sensors is engaged; generating informationcorresponding to the detected activity in which the user of the one ormore gesture sensors is engaged; and controlling, based on providing thegenerated information to a second device for monitoring a physiologicalcharacteristic of the user, adjustment of a performance mode of thesecond device for monitoring the physiological characteristic of theuser, the second device being separate from the first device.
 2. Thesystem of claim 1, wherein the one or more processor-readable mediafurther store instructions which, when executed by the one or moreprocessors, cause performance of: obtaining physiological characteristicdata generated by the second device in the adjusted performance mode;and regulating medication dosage based on the obtained physiologicalcharacteristic data generated by the second device in the adjustedperformance mode.
 3. The system of claim 1, wherein the one or moreprocessor-readable media further store instructions which, when executedby the one or more processors, cause performance of: regulatingmedication dosage based on the detected activity in which the user ofthe one or more gesture sensors is engaged.
 4. The system of claim 1,wherein the detected activity is a meal consumption activity.
 5. Thesystem of claim 1, wherein the generated information corresponding tothe detected activity comprises an identification of the detectedactivity.
 6. The system of claim 1, wherein the generated informationcorresponding to the detected activity is formatted to cause theadjustment of the performance mode of the second device for monitoringthe physiological characteristic of the user.
 7. The system of claim 1,wherein the physiological characteristic of the user is a glucose levelof the user.
 8. The system of claim 1, wherein controlling theadjustment of the performance mode comprises causing a change in anamount of power supplied to the second device for monitoring thephysiological characteristic of the user.
 9. The system of claim 1,wherein controlling the adjustment of the performance mode comprisescausing a change in a sampling rate of the second device for monitoringthe physiological characteristic of the user.
 10. The system of claim 1,wherein controlling the adjustment of the performance mode comprisescausing a change in timing of output generated by the second device formonitoring the physiological characteristic of the user.
 11. Aprocessor-implemented method comprising: detecting, based on processingsensor data obtained from one or more gesture sensors included in afirst device, an activity in which a user of the one or more gesturesensors is engaged; generating information corresponding to the detectedactivity in which the user of the one or more gesture sensors isengaged; and controlling, based on providing the generated informationto a second device for monitoring a physiological characteristic of theuser, adjustment of a performance mode of the second device formonitoring the physiological characteristic of the user, the seconddevice being separate from the first device.
 12. The method of claim 11,further comprising: obtaining physiological characteristic datagenerated by the second device in the adjusted performance mode; andregulating medication dosage based on the obtained physiologicalcharacteristic data generated by the second device in the adjustedperformance mode.
 13. The method of claim 11, wherein the detectedactivity is a meal consumption activity.
 14. The method of claim 11,wherein the physiological characteristic of the user is a glucose levelof the user.
 15. The method of claim 11, wherein controlling theadjustment of the performance mode comprises causing a change in anamount of power supplied to the second device for monitoring thephysiological characteristic of the user.
 16. The method of claim 11,wherein controlling the adjustment of the performance mode comprisescausing a change in a sampling rate of the second device for monitoringthe physiological characteristic of the user.
 17. The method of claim11, wherein controlling the adjustment of the performance mode comprisescausing a change in timing of output generated by the second device formonitoring the physiological characteristic of the user.
 18. One or morenon-transitory processor-readable media storing instructions which, whenexecuted by one or more processors, cause performance of: detecting,based on processing sensor data obtained from one or more gesturesensors included in a first device, an activity in which a user of theone or more gesture sensors is engaged; generating informationcorresponding to the detected activity in which the user of the one ormore gesture sensors is engaged; and controlling, based on providing thegenerated information to a second device for monitoring a physiologicalcharacteristic of the user, adjustment of a performance mode of thesecond device for monitoring the physiological characteristic of theuser, the second device being separate from the first device.
 19. Theone or more non-transitory processor-readable media of claim 18, furtherstoring instructions which, when executed by the one or more processors,cause performance of: obtaining physiological characteristic datagenerated by the second device in the adjusted performance mode; andregulating medication dosage based on the obtained physiologicalcharacteristic data generated by the second device in the adjustedperformance mode.
 20. The one or more non-transitory processor-readablemedia of claim 18, wherein controlling the adjustment of the performancemode comprises: causing a change in an amount of power supplied to thesecond device for monitoring the physiological characteristic of theuser; causing a change in a sampling rate of the second device formonitoring the physiological characteristic of the user; or causing achange in timing of output generated by the second device for monitoringthe physiological characteristic of the user.